Making development simple. The genetic deterministic hypothesis for economic development

This paper discusses the dangers inherent in allempting to simplify something as complex as development. It does this by exploring the Lynn and Vanhanen theory of deterministic development which asserts that varying levels of economic development seen between countries can be explained by differences in 'national intelligence' (national IQ). Assuming that intelligence is genetically determined, and as different races have been shown to have different IQ, then they argue that economic development (measured as GDP/capita) is largely a function of race and interventions to address imbalances can only have a limited impact. The paper presents the Lynne and Vanhanen case and critically discusses the data and analyses (linear regression) upon which it is based. It also extends the cause-effect basis of Lynne and Vanhanen's theory for economic development into human development by using the Human Development Index (HDI). It is argued that while there is nothing mathematically incorrect with their calculations, there are concerns over the data they employ. Even more fundamentally it is argued that statistically significant correlations between the various components of the HDI and national IQ can occur via a host of cause-effect pathways, and hence the genetic determinism theory is far from proven. The paper ends by discussing the dangers involved in the use of over-simplistic measures of development as a means of exploring cause-effect relationships. While the creators of development indices such as the HDI have good intentions, simplistic indices can encourage simplistic explanations of under-development. (c) 2005 Elsevier B.V. All rights reserved.

The geography of tyranny and despair: development indicators and the hypothesis of genetic inevitability of national inequality

Development geography has long sought to understand why inequalities exist and the best ways to address them. Dependency theory sets out an historical rationale for under development based on colonialism and a legacy of developed core and under-developed periphery. Race is relevant in this theory only insofar that Europeans are white and the places they colonised were occupied by people with darker skin colour. There are no innate biological reasons why it happened in that order. However, a new theory for national inequalities proposed by Lynn and Vanhanen in a series of publications makes the case that poorer countries have that status because of a poorer genetic stock rather than an accident of history. They argue that IQ has a genetic basis and IQ is linked to ability. Thus races with a poorer IQ have less ability, and thus national IQ can be positively correlated with performance as measured by an indicator like GDP/capita. Their thesis is one of despair, as little can be done to improve genetic stock significantly other than a programme of eugenics. This paper summarises and critiques the Lynn and Vanhanen hypothesis and the assumptions upon which it is based, and uses this analysis to show how a human desire to simplify in order to manage can be dangerous in development geography. While the attention may naturally be focused on the 'national IQ' variables as a proxy measure of 'innate ability', the assumption of GDP per capita as an indicator of 'success' and 'achievement' is far more readily accepted without criticism. The paper makes the case that the current vogue for indicators, indices and cause-effect can be tyrannical.

Simple sequence repeats reveal uneven distribution of genetic diversity in chloroplast genomes of Brassica oleracea L. and (n=9) wild relatives

Diversity in the chloroplast genome of 171 accessions representing the Brassica 'C' (n = 9) genome, including domesticated and wild B. oleracea and nine inter-fertile related wild species, was investigated using six chloroplast SSR (microsatellite) markers. The lack of diversity detected among 105 cultivated and wild accessions of B. oleracea contrasted starkly with that found within its wild relatives. The vast majority of B. oleracea accessions shared a single haplotype, whereas as many as six haplotypes were detected in two wild species, B. villosa Biv. and B. cretica Lam.. The SSRs proved to be highly polymorphic across haplotypes, with calculated genetic diversity values (H) of 0.23-0.87. In total, 23 different haplotypes were detected in C genome species, with an additional five haplotypes detected in B. rapa L. (A genome n = 10) and another in B. nigra L. (B genome, n = 8). The low chloroplast diversity of B. oleracea is not suggestive of multiple domestication events. The predominant B. oleracea haplotype was also common in B. incana Ten. and present in low frequencies in B. villosa, B. macrocarpa Guss, B. rupestris Raf. and B. cretica. The chloroplast SSRs reveal a wealth of diversity within wild Brassica species that will facilitate further evolutionary and phylogeographic studies of this important crop genus.

The transcriptome of Populus in elevated CO2

The consequences of increasing atmospheric carbon dioxide for long-term adaptation of forest ecosystems remain uncertain, with virtually no studies undertaken at the genetic level. A global analysis using cDNA microarrays was conducted following 6 yr exposure of Populus × euramericana (clone I-214) to elevated [CO2] in a FACE (free-air CO2 enrichment) experiment.• Gene expression was sensitive to elevated [CO2] but the response depended on the developmental age of the leaves, and < 50 transcripts differed significantly between different CO2 environments. For young leaves most differentially expressed genes were upregulated in elevated [CO2], while in semimature leaves most were downregulated in elevated [CO2].• For transcripts related only to the small subunit of Rubisco, upregulation in LPI 3 and downregulation in LPI 6 leaves in elevated CO2 was confirmed by anova. Similar patterns of gene expression for young leaves were also confirmed independently across year 3 and year 6 microarray data, and using real-time RT–PCR.• This study provides the first clues to the long-term genetic expression changes that may occur during long-term plant response to elevated CO2.

Pollination ecology in the 21st century: key questions for future research

To inspire new ideas in research on pollination ecology, we list the most important unanswered questions in the field. This list was drawn up by contacting 170 scientists from different areas of pollination ecology and asking them to contribute their opinion on the greatest knowledge gaps that need to be addressed. Almost 40% of them took part in our email poll and we received more than 650 questions and comments, which we classified into different categories representing various aspects of pollination research. The original questions were merged and synthesised, and a final vote and ranking led to the resultant list. The categories cover plant sexual reproduction, pollen and stigma biology, abiotic pollination, evolution of animal-mediated pollination, interactions of pollinators and floral antagonists, pollinator behaviour, taxonomy, plant-pollinator assemblages, geographical trends in diversity, drivers of pollinator loss, ecosystem services, management of pollination, and conservation issues such as the implementation of pollinator conservation. We focused on questions that were of a broad scope rather than case-specific; thus, addressing some questions may not be feasible within single research projects but constitute a general guide for future directions. With this compilation we hope to raise awareness of pollination-related topics not only among researchers but also among non-specialists including policy makers, funding agencies and the public at large.

Quantifying the impact and relevance of scientific research

Qualitative and quantitative methods are being developed to measure the impacts of research on society, but they suffer from serious drawbacks associated with linking a piece of research to its subsequent impacts. We have developed a method to derive impact scores for individual research publications according to their contribution to answering questions of quantified importance to end users of research. To demonstrate the approach, here we evaluate the impacts of research into means of conserving wild bee populations in the UK. For published papers, there is a weak positive correlation between our impact score and the impact factor of the journal. The process identifies publications that provide high quality evidence relating to issues of strong concern. It can also be used to set future research agendas.

A nipple shield delivery system for oral drug delivery to breastfeeding infants : Microbicide delivery to inactivate HIV

A new drug delivery method for infants is presented which incorporates an active pharmaceutical ingredient (API)-loaded insert into a nipple shield delivery system (NSDS). The API is released directly into milk during breastfeeding. This study investigates the feasibility of using the NSDS to deliver the microbicide sodium dodecyl sulfate (SDS), with the goal of preventing mother-to-child transmission (MTCT) of HIV during breastfeeding in low-resource settings, when there is no safer alternative for the infant but to breastfeed. SDS has been previously shown to effectively inactivate HIV in human milk. An apparatus was developed to simulate milk flow through and drug release from a NSDS. Using this apparatus milk was pulsed through a prototype device containing a non-woven fiber insert impregnated with SDS and the microbicide was rapidly released. The total SDS release from inserts ranged from 70 to 100% of the average 0.07 g load within 50 ml (the volume of a typical breastfeed). Human milk spiked with H9/HIVIIIB cells was also passed through the same set-up. Greater than 99% reduction of cell-associated HIV infectivity was achieved in the first 10 ml of milk. This proof of concept study demonstrates efficient drug delivery to breastfeeding infants is achievable using the NSDS.

Graph-based algorithms for comparison and prediction of household-level energy use profiles

We present an efficient graph-based algorithm for quantifying the similarity of household-level energy use profiles, using a notion of similarity that allows for small time–shifts when comparing profiles. Experimental results on a real smart meter data set demonstrate that in cases of practical interest our technique is far faster than the existing method for computing the same similarity measure. Having a fast algorithm for measuring profile similarity improves the efficiency of tasks such as clustering of customers and cross-validation of forecasting methods using historical data. Furthermore, we apply a generalisation of our algorithm to produce substantially better household-level energy use forecasts from historical smart meter data.