Behavioral decisions based on a trade-off between foraging and vigilance or hiding require information. I studied how the amount of information about predators influenced yellowhammers'' (Emberiza citrinella) foraging delay and alert perching behavior. Yellowhammers were shown a flying sparrowhawk (Accipiter nisus) silhouette, which elicited alarm calls, or a square piece of wood (control), which elicited flight calls. Yellowhammers that could not see the sparrowhawk model, but heard the alarm calls, had less complete information about the predation risk than those that actually saw the sparrowhawk. Hearing alarm calls affected the behavior of yellowhammers. Birds with less complete information about the predator exhibited alert perching more often immediately after the encounter than did birds that saw the sparrowhawk model. Also, birds that saw the sparrowhawk resumed foraging earliest, while birds that heard the alarm calls resumed foraging latest. Although there was a tendency for a significant difference in body mass between dominant and sub-dominant individuals, there was no significant difference in foraging delay. Both the foraging delay and the increase in alert perching caused lost feeding opportunities. Completeness of information and its effect on decision-making may thus affect the fitness of an animal
Statistical innovations allow clinicians to estimate personalized networks from longitudinal data, for example data collected via the Experience Sampling Method (ESM). Such networks can generate insights that may be relevant for constructing case formulations, and therefore guide the selection of personalized treatment targets. While the notion of personalized networks aligns well with the way clinicians think and reason, there are currently several barriers to clinical implementation that limit the utility of such models. First, the most popular network estimation routines are data-driven and do not allow clinicians to incorporate their expertise and theory. Second, network models have many parameters, which can make accurate estimation challenging. Finally, network estimation requires technical skills that are not regularly taught in clinical programs. In this article, we introduce PREMISE, an approach that formally integrates case formulations with personalized network estimation. Using prior elicitation techniques, clinical working hypotheses are translated into formal models, which can subsequently inform network estimation from ESM data using Bayesian inference. PREMISE tackles the three challenges described above: Incorporating clinical information into network estimation systematically allows theoretical and data-driven integration, which in turn increases the accuracy of network estimation techniques. In addition, we implemented the principles of PREMISE into a practical web-based toolkit that generates intuitive feedback, thereby facilitating clinical implementation. To illustrate its clinical potential, we use PREMISE to estimate clinically informed networks for a client suffering from obsessive-compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation.
The most common complication of continuous subcutaneous insulin infusion (CSII) is inflammation at the infusion site. To determine possible risk factors to these infections, we studied several factors in the management of CSII and compared the pyogenic skin inflammation rate, the carriage rate of Staphylococcus aureus, and the HbA1 level among 50 CSII-treated diabetic patients, 50 diabetic patients on insulin injections, 48 diabetic patients on oral medication, and 40 healthy volunteers. There was no increased carriage rate of S. aureus among CSII-treated patients (42%) as compared with the other groups. An unexpected inverse relationship existed between HbA1 level and carriage rate in the CSII-treated group (HbA1 5-8%, n = 16, 69%; HbA1 8-10% n = 15, 40%; HbA1 greater than 10, n = 19, 21% P = .02). Pyogenic skin inflammations were reported by 24 (48%) CSII-treated patients, of which 18 had infected infusion sites, 6 (12%) insulin injecting patients, 2 (4%) patients on oral medication, and 3 (8%) healthy volunteers (P less than .01). The occurrence of inflamed infusion sites was not associated with carriage of S. aureus, the indwelling time of the needle, or the insulin dosage per day. There was an association, however, with the type of insulin preparation classified according to the added preservative: m-cresol-containing insulin (n = 24, 54%); methyl p-hydroxybenzoate-containing insulin (n = 26, 19%, P = .02). We concluded that the carriage of S. aureus is not increased among diabetic patients on CSII treatment and is not a risk factor in the occurrence of inflammation at the infusion site.(ABSTRACT TRUNCATED AT 250 WORDS)
The evaluation of measurement uncertainty in accordance with the 'Guide to the expression of uncertainty in measurement' (GUM) has not yet become widespread in physical chemistry. With only the law of the propagation of uncertainty from the GUM, many of these uncertainty evaluations would be cumbersome, as models are often non-linear and require iterative calculations. The methods from GUM supplements 1 and 2 enable the propagation of uncertainties under most circumstances. Experimental data in physical chemistry are used, for example, to derive reference property data and support trade-all applications where measurement uncertainty plays an important role. This paper aims to outline how the methods for evaluating and propagating uncertainty can be applied to some specific cases with a wide impact: deriving reference data from vapour pressure data, a flash calculation, and the use of an equation-of-state to predict the properties of both phases in a vapour-liquid equilibrium. The three uncertainty evaluations demonstrate that the methods of GUM and its supplements are a versatile toolbox that enable us to evaluate the measurement uncertainty of physical chemical measurements, including the derivation of reference data, such as the equilibrium thermodynamical properties of fluids.
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