Model Organisms are not (Theoretical) Models

Many biological investigations are organized around a small group of species, often referred to as “model organisms”, such as the fruit fly Drosophila melanogaster. The terms “model” and “modeling” also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka-Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. Theoretical modeling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account

Model Organisms are not (Theoretical) Models

Many biological investigations are organized around a small group of species, often referred to as “model organisms”, such as the fruit fly Drosophila melanogaster. The terms “model” and “modeling” also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka-Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. Theoretical modeling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account

Why Experiments Matter

Experimentation is traditionally considered a privileged means of confirmation. However, how experiments are a better confirmatory source than other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. ‘Specimenhood’ involves possessing relevant properties given the investigative target and the hypothesis in question. Specimens are thus representative members of a class of systems, to which a hypothesis refers. It is in virtue of both control and specimenhood that experiments provide powerful confirmatory evidence. This explains the distinctive power of experiments: although modellers exert extensive control, they do not exert this control over specimens; although natural experiments utilize specimens, control is diminished

Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States

We present the data on wealth and income distributions in the United Kingdom, as well as on the income distributions in the individual states of the USA. In all of these data, we find that the great majority of population is described by an exponential distribution, whereas the high-end tail follows a power law. The distributions are characterized by a dimensional scale analogous to temperature. The values of temperature are determined for the UK and the USA, as well as for the individual states of the USA.Comment: 8 pages, 6 figures, elsart.cls. Submitted to Physica A, proceedings of NATO workshop Applications of Physics in Economic Modeling, Prague, February 2001. V.2: minor stylistic expansio

Utility values for symptomatic non-severe hypoglycaemia elicited from persons with and without diabetes in Canada and the United Kingdom

<p>Abstract</p> <p>Objective</p> <p>To elicit societal and patient utilities associated with diabetic symptomatic non-severe hypoglycaemia for three health states: 1) rare (quarterly), 2) intermittent (monthly), 3) and frequent (weekly) hypoglycaemia episodes.</p> <p>Methods</p> <p>Using validated health states, time trade-off utilities were elicited from 51 Canadian respondents with diabetes, and 79 respondents in Canada and 75 respondents in the United Kingdom (UK) without diabetes.</p> <p>Results and discussion</p> <p>Each hypoglycaemic episode was associated with a reduction in utility and persons with diabetes consistently reported slightly higher utility values than respondents without diabetes. The utility for diabetes without hypoglycaemia ranged from 0.88 to 0.97, the mean utility for rare hypoglycaemic events (quarterly) ranged between 0.85 and 0.94. The utility for the intermittent state (monthly) ranged from 0.77 to 0.90 and from 0.66 to 0.0.83 for the frequent state (weekly). Differences were observed between respondents without diabetes in Canada and the UK. Using a multivariate linear OLS regression, the estimated utilities associated with a single hypoglycaemic event were -0.0033 and -0.0032 for respondents with diabetes and without diabetes, respectively.</p> <p>Conclusion</p> <p>Among respondents with and without diabetes, there was a demonstrable utility loss associated with hypoglycaemia. Considering a utility loss of 0.03 as a minimum clinically important difference for persons with diabetes, the evidence from this study indicates that as low as ten symptomatic non-severe hypoglycaemic episodes per year may be of clinical importance and that the importance increases with frequency of episodes. Integrating directly elicited utility values such as those reported here will improve the quality and applicability of economic evaluations of diabetes treatment.</p

The Impact of Differential Cost Sharing of Non-Steroidal Anti-Inflammatory Agents on the Use and Costs of Analgesic Drugs

OBJECTIVE: To estimate the effect of differential cost sharing (DCS) schemes for non-steroidal anti-inflammatory drugs (NSAIDs) on drug subsidy program and beneficiary expenditures. DATA SOURCES/STUDY SETTING: Monthly aggregate claims data from Pharmacare, the public drug subsidy program for seniors in British Columbia, Canada over the period 1989-11 to 2001-06. STUDY DESIGN: DCS limits insurance reimbursement of a group of therapeutically similar drugs to the cost of the lowest priced drugs, with beneficiaries responsible for costs above the reimbursement limit. Pharmacare introduced two different forms of DCS, generic substitution (GS) and reference pricing (RP), in April 1994 and November 1995, respectively, to the NSAIDs. Under GS, generic and brand versions of the same NSAID are considered interchangeable, whereas under RP different NSAIDs are. We extrapolated average reimbursement per day of NSAID therapy over the months before GS and RP to estimate what expenditures would have been without the policies. These counterfactual predictions were compared to actual values to estimate the impact of the policies; the estimated impacts on reimbursement rates were multiplied by the post-policy volume of NSAIDS dispensed, which appeared unaffected by the policies, to estimate expenditure changes. DATA COLLECTION: The cleaned NSAID claims data, obtained from Pharmacare’s databases, were aggregated by month and by their reimbursement status under the GS and RP policies. PRINCIPAL FINDINGS: After RP, program expenditures declined by $22.7 million, or$4 million annually, cutting expenditure by half. Most savings accrued from the substitution of low cost NSAIDs for more costly alternatives. About 20% of savings represented expenditures by seniors who elected to pay for partially-reimbursed drugs. GS produced one quarter the savings of RP. CONCLUSIONS: RP of NSAIDs achieved its goal of reducing drug expenditures and was more effective than GS. The effects of RP on patient health and associated health care costs remain to be investigated.Reference pricing; generic substitution; prescription drugs; drug cost containment; NSAIDs.

Non-destructive testing of dissimilar friction stir welds

friction stir welds between 5754 aluminium alloy and C11000 copper. The Friction stir welds of 5754 aluminium alloy and C11000 copper were produced at different tool rotational speeds and feed rates. The tool rotational speed was varied between 600 and 1200 rpm while the feed rate was varied between 50 and 300 mm/min. The visual inspection and the x-ray radiographic testing techniques were employed to conduct the tests; these tests were conducted on the welds to ascertain the joint integrity before characterization to have an idea of the quality of the welds. No visual defects were observed on all the welds considered but the x-ray radiography technique revealed the presence of wormhole defects and discontinuities in some of the welds. It was found that the welds produced at 950 rpm with varied feed rates were the best quality welds produced and this was substantiated with the microstructural evaluation of the joint interface. It was found that these welds have good mixing and metallurgical bonding at the interfaces

VARiD: A variation detection framework for color-space and letter-space platforms

Motivation: High-throughput sequencing (HTS) technologies are transforming the study of genomic variation. The various HTS technologies have different sequencing biases and error rates, and while most HTS technologies sequence the residues of the genome directly, generating base calls for each position, the Applied Biosystem's SOLiD platform generates dibase-coded (color space) sequences. While combining data from the various platforms should increase the accuracy of variation detection, to date there are only a few tools that can identify variants from color space data, and none that can analyze color space and regular (letter space) data together