OFT ascent/descent ancillary data requirements document

Requirements are presented for the ascent/descent (A/D) navigation and attitude-dependent ancillary data products to be generated for the space shuttle orbiter in support of orbital flight test requirements, MPAD guidance and navigation performance assessment, and the mission evaluation team. It was intended that this document serve as the sole requirements control instrument between MPB/MPAD and the A/D ancillary data users. The requirements are primarily functional in nature, but some detail level requirements are also included

ELECTROKINETIC PHENOMENA : VI. RELATIONSHIP BETWEEN ELECTRIC MOBILITY, CHARGE, AND TITRATION OF PROTEINS

1. By combining the theories of Smoluchowski, Debye and Hückel, and Henry it is possible to state explicitly (making necessary assumptions) under what conditions the following simple rule should be valid for proteins: In solutions of the same ionic strength, the electric mobilities of the same protein at different hydrogen ion activities should be proportional to the number of hydrogen (hydroxyl) ions bound. 2. Data of Tiselius and of the writer confirm this rule for (a) egg albumin, (b) serum albumin, (c) deaminized gelatin and gelatin, and (d) casein. 3. On the basis of the confirmed theory the titration curves of certain proteins are predicted from their mobilities. 4. It is shown that when certain proteins are adsorbed by quartz the apparent dissociation constant of the adsorbed protein is practically unchanged. The mass law must also be valid at the phase boundary. 5. The facts of paragraphs (1) to (4) are discussed in connection with the mechanism of (a) protein adsorption, (b) enzyme activity, (c) immune reactions, (d) the calculation of the electric charge of cells, and (e) criteria of surface similarity

Approaches to decision-making among late-stage melanoma patients: a multifactorial investigation.

PurposeThe treatment decisions of melanoma patients are poorly understood. Most research on cancer patient decision-making focuses on limited components of specific treatment decisions. This study aimed to holistically characterize late-stage melanoma patients' approaches to treatment decision-making in order to advance understanding of patient influences and supports.Methods(1) Exploratory analysis of longitudinal qualitative data to identify themes that characterize patient decision-making. (2) Pattern analysis of decision-making themes using an innovative method for visualizing qualitative data: a hierarchically-clustered heatmap. Participants were 13 advanced melanoma patients at a large academic medical center.ResultsExploratory analysis revealed eight themes. Heatmap analysis indicated two broad types of patient decision-makers. "Reliant outsiders" relied on providers for medical information, demonstrated low involvement in decision-making, showed a low or later-in-care interest in clinical trials, and expressed altruistic motives. "Active insiders" accessed substantial medical information and expertise in their networks, consulted with other doctors, showed early and substantial interest in trials, demonstrated high involvement in decision-making, and employed multiple decision-making strategies.ConclusionWe identified and characterized two distinct approaches to decision-making among patients with late-stage melanoma. These differences spanned a wide range of factors (e.g., behaviors, resources, motivations). Enhanced understanding of patients as decision-makers and the factors that shape their decision-making may help providers to better support patient understanding, improve patient-provider communication, and support shared decision-making

Regression of ranked responses when raw responses are censored

We discuss semiparametric regression when only the ranks of responses are observed. The model is $Y_i = F (\mathbf{x}_i'{\boldsymbol\beta}_0 + \varepsilon_i)$, where $Y_i$ is the unobserved response, $F$ is a monotone increasing function, $\mathbf{x}_i$ is a known $p-$vector of covariates, ${\boldsymbol\beta}_0$ is an unknown $p$-vector of interest, and $\varepsilon_i$ is an error term independent of $\mathbf{x}_i$. We observe $\{(\mathbf{x}_i,R_n(Y_i)) : i = 1,\ldots ,n\}$, where $R_n$ is the ordinal rank function. We explore a novel estimator under Gaussian assumptions. We discuss the literature, apply the method to an Alzheimer's disease biomarker, conduct simulation studies, and prove consistency and asymptotic normality.Comment: 33 pages, 6 figure