The evolution of technicity: whence creativity and innovation

This paper introduces the concept of ‘technicity’, a term borrowed from philosophy but recast in an Darwinian mould. Firstly, however, the presumption that language is THE unique and pre-eminent human trait is put to the adaptationist test. Evidence from palaeontology, primate studies and evolutionary psychology is brought together to (tentatively) suggest that language (speech) has a deep evolutionary past and that all members of the genus Homo possessed speech in some form. The second section marshals evidence that suggests our species possesses a new ‘making things’ adaptation. This adaptation appears to be the basis for the speciation event that defines behaviourally modern humans: our species. This is the capability for which the term ‘technicity’ is appropriated. The argument for splitting off language from technicity uses the concept of the extended phenotype. Technicity might best be characterised by a creative capacity to: a) deconstruct and reconstruct nature, and b) communicate by drawing. The notion is floated that the newly evolved adaptation discretely insinuated itself into extant human culture; followed by brief consideration of the role of drawing, in the form of writing, on the precision and power of linguistic expression. It is suggested that technicity might usefully be considered the source of our intellect and language its whetstone. If further studies support the technicity hypothesis then reappraisal of conceptual framework underpinning the educational curriculum might be of benefit: a technology of language rather than the language of technology

Strength of association in lagged vs. temporal proximity models–odds ratios.

<p><b>Note.</b> 3-level mixed effect model (relatedness of measures/ subjects/ Trust) adjusted for age, gender, ethnicity, primary diagnosis at baseline. <i>AOR</i> adjusted odds ratio. <i>95% CI</i> 95 percent confidence interval. <i>P</i> level of statistical significance.</p><p>Strength of association in lagged vs. temporal proximity models–odds ratios.</p

Explanatory variables–SAPROF.

<p><b>Note.</b> 3-level mixed effect model (relatedness of measures/ subjects/ Trust) adjusted for age, gender, ethnicity, primary diagnosis at baseline. <i>AOR</i> adjusted odds ratio. <i>95% CI</i> 95 percent confidence interval. <i>% change</i> percentage of change from baseline coefficient to coefficient adjusted for explanatory variable. <i>P</i> level of statistical significance.</p><p>Explanatory variables–SAPROF.</p

Explanatory variables—HCR-20^v3 clinical and risk management variables.

<p><b>Note.</b> 3-level mixed effect model (relatedness of measures/ subjects/ Trust) adjusted for age, gender, ethnicity, primary diagnosis at baseline. <i>AOR</i> adjusted odds ratio. <i>95% CI</i> 95 percent confidence interval. <i>% change</i> percentage of change from baseline coefficient to coefficient adjusted for explanatory variable. <i>P</i> level of statistical significance.</p><p>Explanatory variables—HCR-20^v3 clinical and risk management variables.</p

Predictive vs. causal models–strength of association.

<p>White bars reflect the predictive / lagged model, grey bars reflect the temporal proximity / causal model. The error bars represent 95 per cent confidence intervals.</p

Inter-item correlations.

<p><b>Note.</b> Numbers in cells are descriptive statistics of Spearman correlation coefficients between each pair of items.</p><p>¹⁾ Statistical significance was based on a Bonferroni corrected alpha (0.05 / number of correlations). <i>M</i> mean, <i>SD</i> standard deviation, <i>Md</i> median.</p><p>Inter-item correlations.</p

Predictive vs. causal models–accuracy.

<p>White bars reflect the predictive / lagged model, grey bars reflect the temporal proximity / causal model. The error bars represent 95 per cent confidence intervals.</p

Predictive accuracy of lagged vs. temporal proximity models—AUC values.

<p><b>Note.</b> 2-level mixed effect model (relatedness of measures/ subjects). <i>AUC</i> area under the ROC curve. <i>95% CI</i> 95 percent confidence interval. <i>P</i> level of statistical significance.</p><p>Predictive accuracy of lagged vs. temporal proximity models—AUC values.</p

Discovery of a Selective and Potent Inhibitor of Mitogen-Activated Protein Kinase-Interacting Kinases 1 and 2 (MNK1/2) Utilizing Structure-Based Drug Design

The discovery of a highly potent and selective small molecule inhibitor <b>9</b> for in vitro target validation of MNK1/2 kinases is described. The aminopyrazine benzimidazole series was derived from an HTS hit and optimized by utilization of a docking model, conformation analysis, and binding pocket comparison against antitargets

Discovery of a Selective and Potent Inhibitor of Mitogen-Activated Protein Kinase-Interacting Kinases 1 and 2 (MNK1/2) Utilizing Structure-Based Drug Design

The discovery of a highly potent and selective small molecule inhibitor <b>9</b> for in vitro target validation of MNK1/2 kinases is described. The aminopyrazine benzimidazole series was derived from an HTS hit and optimized by utilization of a docking model, conformation analysis, and binding pocket comparison against antitargets