Attitudes and Stereotypes in Lung Cancer versus Breast Cancer.

Societal perceptions may factor into the high rates of nontreatment in patients with lung cancer. To determine whether bias exists toward lung cancer, a study using the Implicit Association Test method of inferring subconscious attitudes and stereotypes from participant reaction times to visual cues was initiated. Participants were primarily recruited from an online survey panel based on US census data. Explicit attitudes regarding lung and breast cancer were derived from participants' ratings (n = 1778) regarding what they thought patients experienced in terms of guilt, shame, and hope (descriptive statements) and from participants' opinions regarding whether patients ought to experience such feelings (normative statements). Participants' responses to descriptive and normative statements about lung cancer were compared with responses to statements about breast cancer. Analyses of responses revealed that the participants were more likely to agree with negative descriptive and normative statements about lung cancer than breast cancer (P<0.001). Furthermore, participants had significantly stronger implicit negative associations with lung cancer compared with breast cancer; mean response times in the lung cancer/negative conditions were significantly shorter than in the lung cancer/positive conditions (P<0.001). Patients, caregivers, healthcare providers, and members of the general public had comparable levels of negative implicit attitudes toward lung cancer. These results show that lung cancer was stigmatized by patients, caregivers, healthcare professionals, and the general public. Further research is needed to investigate whether implicit and explicit attitudes and stereotypes affect patient care.

PyEvolve: a toolkit for statistical modelling of molecular evolution.

BACKGROUND: Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences - ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation. RESULTS: Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from approximately 10 days to approximately 6 hours. CONCLUSION: PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The toolkit can be used interactively or by writing and executing scripts. The toolkit uses efficient processes for specifying the parameterisation of statistical models, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware. PyEvolve can be readily adapted in response to changing computational demands and hardware configurations to maximise performance. PyEvolve is released under the GPL and can be downloaded from http://cbis.anu.edu.au/software.