Design and Estimation for the Population Prevalence of Infectious Diseases

Understanding the prevalence of infections in the population of interest is critical for making data-driven public health responses to infectious disease outbreaks. Accurate prevalence estimates, however, can be difficult to calculate due to a combination of low population prevalence, imperfect diagnostic tests, and limited testing resources. In addition, strategies based on convenience samples that target only symptomatic or high-risk individuals will yield biased estimates of the population prevalence. We present Bayesian multilevel regression and poststratification models that incorporate probability sampling designs, the sensitivity and specificity of a diagnostic test, and specimen pooling to obtain unbiased prevalence estimates. These models easily incorporate all available prior information and can yield reasonable inferences even with very low base rates and limited testing resources. We examine the performance of these models with an extensive numerical study that varies the sampling design, sample size, true prevalence, and pool size. We also demonstrate the relative robustness of the models to key prior distribution assumptions via sensitivity analyses.

Interactions between luminance steps and luminance textures for boundary segmentation

ABSTRACTIn natural scenes, two adjacent surfaces may differ in mean luminance without any sharp change in luminance at their boundary, but rather due to different relative proportions of light and dark regions within each surface. We refer to such boundaries as luminance texture boundaries (LTBs), and in this study we investigate interactions between luminance texture boundaries and luminance step boundaries (LSBs) in a segmentation task. Using a simple masking paradigm, we find very little influence of LSB maskers on LTB segmentation thresholds. Similarly, we find only modest effects of LTB maskers on LSB thresholds. By contrast, each kind of boundary strongly masks targets of the same kind. Our data is consistent with the possibility that luminance texture boundaries may be segmented using different mechanisms than those used to segment luminance step boundaries. At the same time, our work also suggests that LTB segmentation is subject to influences from LSBs. We suggest that the relative robustness of LTB segmentation to interference from LSBs may serve the ecologically important role of providing robustness to changes in luminance caused by cast shadows, and we propose future experimental work to investigate this hypothesis.

Robust design from systems physics

A crucial challenge in engineering modern, integrated systems is to produce robust designs. However, quantifying the robustness of a design is less straightforward than quantifying the robustness of products. For products, in particular engineering materials, intuitive, plain language terms of strong versus weak and brittle versus ductile take on precise, quantitative meaning in terms of stress–strain relationships. Here, we show that a “systems physics” framing of integrated system design produces stress–strain relationships in design space. From these stress–strain relationships, we find that both the mathematical and intuitive notions of strong versus weak and brittle versus directly characterize the robustness of designs. We use this to show that the relative robustness of designs against changes in problem objectives has a simple graphical representation. This graphical representation, and its underlying stress–strain foundation, provide new metrics that can be applied to classes of designs to assess robustness from feature- to system-level.

When the presence of a vateritic otolith has morphological effect on its aragonitic partner: trans-lateral compensation induces bias in microecological patterns in one-side-only vateritic otolith

In many teleost species, aragonite is the normal form of calcium carbonate in sagittal otoliths. In the case of one-side-only (OSO) vateritic otoliths (abnormal crystalline structure), morphological investigations are systematically conducted on the other side. The implicit assumption is that the morphological information on the aragonitic side remains unaffected by the presence of vaterite on the other side. However, the extent to which this assumption is met has never been explored. Applying geometric morphometrics on experimentally maintained brown trout (Salmo trutta) revealed incongruent variational patterns and mean shape differences between OSO and two-sided aragonitic otoliths, possibly due to trans-lateral compensation to preserve their auditory function. More specifically, fully aragonitic pairs of otoliths are more prone to exhibit microecological variations compared with OSO vateritic pairs. Simulation emphasizes the relative robustness of both micro- and macroecological effects until a high proportion of OSO vateritic otoliths is used. Given that otolith shape analysis provides a useful basis for stock separation, care should be taken when interpreting otolith shape in the presence of vateritic otoliths.

Where can a place cell put its fields? Let us count the ways

A hippocampal place cell exhibits multiple firing fields within and across environments. What factors determine the configuration of these fields, and could they be set down in arbitrary locations? We conceptualize place cells as performing evidence combination across many inputs and selecting a threshold to fire. Thus, mathematically they are perceptrons, except that they act on geometrically organized inputs in the form of multiscale periodic grid-cell drive, and external cues. We analytically count which field arrangements a place cell can realize with structured grid inputs, to show that many more place-field arrangements are realizable with grid-like than one-hot coded inputs. However, the arrangements have a rigid structure, defining an underlying response scaffold. We show that the “separating capacity” or spatial range over which all potential field arrangements are realizable equals the rank of the grid-like input matrix, which in turn equals the sum of distinct grid periods, a small fraction of the unique grid-cell coding range. Learning different grid-to-place weights beyond this small range will alter previous arrangements, which could explain the volatility of the place code. However, compared to random inputs over the same range, grid-structured inputs generate larger margins, conferring relative robustness to place fields when grid input weights are fixed.Significance statementPlace cells encode cognitive maps of the world by combining external cues with an internal coordinate scaffold, but our ability to predict basic properties of the code, including where a place cell will exhibit fields without external cues (the scaffold), remains weak. Here we geometrically characterize the place cell scaffold, assuming it is derived from multiperiodic modular grid cell inputs, and provide exact combinatorial results on the space of permitted field arrangements. We show that the modular inputs permit a large number of place field arrangements, with robust fields, but also strongly constrain their geometry and thus predict a structured place scaffold.

A Comparison of Robust Criteria for Vehicle Routing Problem with Soft Time Windows

In this study, we focus on robust criteria for vehicle routing problems with soft time windows (VRPSTW). The main objective is to find a robust solution that provides the best for the worst case performance for VRPSTW under uncertain travel times. The robust criteria are used in this study such as absolute robustness, robust deviation, and relative robustness as a basis for comparison. The VRPSTW becomes complex when the travel times are uncertain. This uncertainty can be caused by traffic jams, accidents, or inclement weather conditions. The experiment uses benchmarking problems. The number of scenarios is generated randomly into intervals of travel time, equal to 4, 6, and 8 instances for each problem set. Each set of problem instances can be denoted by the percentage of uncertainty α, equal to 0.2, 0.4, 0.6, and 0.8. This study will demonstrate that the most indicated robust criteria for these situations are robust deviation and relative robustness. The most important part of the decision maker is to determine the uncertainty percentage to cover all uncertainties that need to be considered.