A passive-active dynamic insulation system for all climates

Peer reviewedPublisher PD

Portal vein thrombosis and arterioportal shunts : effects on tumor response after chemoembolization of hepatocellular carcinoma

AIM: To evaluate the effect of portal vein thrombosis and arterioportal shunts on local tumor response in advanced cases of unresectable hepatocellular carcinoma treated by transarterial chemoembolization. METHODS: A retrospective study included 39 patients (mean age: 66.4 years, range: 45-79 years, SD: 7) with unresectable hepatocellular carcinoma (HCC) who were treated with repetitive transarterial chemoembolization (TACE) in the period between March 2006 and October 2009. The effect of portal vein thrombosis (PVT) (in 19 out of 39 patients), the presence of arterioportal shunt (APS) (in 7 out of 39), the underlying liver pathology, Child-Pugh score, initial tumor volume, number of tumors and tumor margin definition on imaging were correlated with the local tumor response after TACE. The initial and end therapy local tumor responses were evaluated according to the response evaluation criteria in solid tumors (RECIST) and magnetic resonance imaging volumetric measurements. RESULTS: The treatment protocols were well tolerated by all patients with no major complications. Local tumor response for all patients according to RECIST criteria were partial response in one patient (2.6%), stable disease in 34 patients (87.1%), and progressive disease in 4 patients (10.2%). The MR volumetric measurements showed that the PVT, APS, underlying liver pathology and tumor margin definition were statistically significant prognostic factors for the local tumor response (P = 0.018, P = 0.008, P = 0.034 and P = 0.001, respectively). The overall 6-, 12- and 18-mo survival rates from the initial TACE were 79.5%, 37.5% and 21%, respectively. CONCLUSION: TACE may be exploited safely for palliative tumor control in patients with advanced unresectable HCC; however, tumor response is significantly affected by the presence or absence of PVT and APS

Structure Selection from Streaming Relational Data

Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting model is validated, and the cycle repeats as the engineer adjusts the set of features. This paper seeks to streamline application development in large relational domains by introducing a light-weight approach that efficiently evaluates relational features on pieces of the relational graph that are streamed to it one at a time. We evaluate our approach on two social media tasks and demonstrate that it leads to more accurate models that are learned faster

Population ecology of Myzus Persicae (Sulzer)

Imperial Users onl

The split-plot design with covariance

A split-plot data structure is usually modelled by a linear classificatory model with a 0,1 model matrix and with error consisting additively of independent Gaussian errors. Statistical analysis of such a data structure in the usual mode involves then two components of error variance. The usual model is then a special case of what is commonly called a mixed linear model. Consequently, the well-known problems of mixed linear models are encountered. However, the standard balance split-plot data structure has special features of balance that enable progress, as will be explained;With the presence of a concomitant variable and the assumed error structure, the problem of estimation of the dependence of the observations to be explained on the concomitant variable becomes complicated. The model considered is y(,ijk) = (mu) + (alpha)(,i) + (gamma)(,j) + (nu)(,k) + (eta)(,jk) + x(,ijk)c + e(,ij) + s(,ijk) where x(,ijk) is the value of the concomitant variable or the covariate, c is the regression coefficient, e(,ij) and s(,ijk) are errors. The difficulties arise from the existence of the two types of error;The nature of split-plot designs will be exposited, along with special features arising from the balance in the structure;The problem considered is estimation of c, the regression coefficient, because if this is solved, the remainder of the problem of fitting the model seems clear. There is no best way of estimating c because var(e(,ij)) and var(s(,ijk)) are not known and various methods of doing this are discussed;The problem is simple if the ratio of the two variance components is known. So, attention is turned to estimation of a basic parameter related to this ratio, with consideration of various methods, including Bayesian estimation;The widely used method of maximum likelihood fitting of the model is examined. Also, a method of restricted maximum likelihood estimation is examined;Residual problems such as attaching reasonable standard errors to estimates are not solved. It seems that understanding these problems can be achieved only by simulation

Improving Building Fabric Energy Efficiency in Hot-Humid Climates using Dynamic Insulation

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