Effects of pedagogical interventions with visual input enhancement on grammar learning have been investigated by a number of researchers during the past decade and a half. The present review delineates this research domain via a systematic synthesis of 16 primary studies (comprising 20 unique study samples) retrieved through an exhaustive literature search. The overall magnitude of visual input enhancement was addressed by calculating and aggregating effect sizedvalues. The results indicate that second language readers provided with enhancement-embedded texts barely outperformed those who were exposed to unenhanced texts with the same target forms flooded in them (d= 0.22). A theoretical tension between form and meaning was indicated by a small but negative effect size value for learners' meaning processing (d= −0.26). The importance of improving methodological practices in this research domain, including the reporting of statistical and treatment-related information and the counteracting of a possible publication bias, was also revealed by the synthetic analyses and is further discussed.

The lack of a suitable in vitro hepatitis B virus (HBV) infectivity model has limited examination of the early stages of the virus-cell interaction. In this study, we used an immortalized cell line derived from human primary hepatocytes, HuS-E/2, to study the mechanism of HBV infection. HBV infection efficiency was markedly increased after dimethyl sulfoxide (DMSO)-induced differentiation of the cells. Transmission electron microscopy demonstrated the presence of intact HBV particles in DMSO-treated HBV-infected HuS-E/2 cells, which could be infected with HBV for up to at least 50 passages. The pre-S1 domain of the large HBsAg (LHBsAg) protein specifically interacted with clathrin heavy chain (CHC) and clathrin adaptor protein AP-2. Short hairpin RNA knockdown of CHC or AP-2 in HuS-E/2 cells significantly reduced their susceptibility to HBV, indicating that both are necessary for HBV infection. Furthermore, HBV entry was inhibited by chlorpromazine, an inhibitor of clathrin-mediated endocytosis. LHBsAg also interfered with the clathrin-mediated endocytosis of transferrin by human hepatocytes. This infection system using an immortalized human primary hepatocyte cell line will facilitate investigations into HBV entry and in devising therapeutic strategies for manipulating HBV-associated liver disorders.

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Helicopter EM resistivity mapping began to be accepted as a means of geologic mapping in the late 1970s. The data were first displayed as plan maps and images. Some 10 years later, sectional resistivity displays became available using the same ''pseudolayer'' half-space resistivity algorithm developed by Fraser and the new centroid depth algorithm developed by Sengpiel. Known as Sengpiel resistivity sections, these resistivity/depth images proved to be popular for the display of helicopter electromagnetic (EM) data in conductive environments. A limitation of the above resistivity and depth algorithms is that the resulting Sengpiel section may imply that the depth of exploration of the EM system is substantially less than is actually the case. For example, a target at depth may be expressed in the raw data, but its appearance on the Sengpiel section may be too shallow (which is a problem with the depth algorithm), or it may not even appear at all (which is a problem with the resistivity algorithm). An algorithm has been adapted from a ground EM analytic method that yields a parameter called the differential resistivity, which is plotted at the differential depth. The technique yields the true resistivity when the half-space is homogeneous. It also tracks a dipping target with greater sensitivity and to greater depth than does the Sengpiel display method. The input parameters are the apparent resistivity and apparent depth from the pseudolayer half-space algorithm and the skin depth for the various frequencies. The output parameters are differential resistivity and differential depth, which are computed from pairs of adjacent frequencies.

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When the objective is to forecast a variable of interest but with many explanatory variables available, one could possibly improve the forecast by carefully integrating them. There are generally two directions one could proceed: combination of forecasts (CF) or combination of information (CI). CF combines forecasts generated from simple models each incorporating a part of the whole information set, while CI brings the entire information set into one super model to generate an ultimate forecast. Through analysis and simulation, we show the relative merits of each, particularly the circumstances where forecast by CF can be superior to forecast by CI, when CI model is correctly specified and when it is misspecified, and shed some light on the success of equally weighted CF. In our empirical application on prediction of monthly, quarterly, and annual equity premium, we compare the CF forecasts (with various weighting schemes) to CI forecasts (with methodology mitigating the problem of parameter proliferation such as principal component approach). We find that CF with (close to) equal weights is generally the best and dominates all CI schemes, while also performing substantially better than the historical mean.