The politics of self-help: The Rockefeller Foundation, philanthropy and the 'long' Green Revolution

While scholars of contemporary philanthropy have observed a concerted interest in the promotion of ‘self-help,’ little has been said about the political history of this investment and its significance in determining both domestic and international development priorities. We locate this modern conceptualisation of self-help in early twentieth-century philanthropic practice that sought to ‘gift’ to individuals and communities the precious habit of self-reliance and social autonomy. The Rockefeller Foundation promoted rural development projects that deliberately sought to ‘emancipate’ the tradition-bound peasant, transforming him or her into a productive, enterprising subject. We begin by documenting their early agricultural extension work, which attempted to spark agrarian change in the US South through the inculcation of modern habits and aspirations among farmers and their families. These agrarian schemes illustrate the newfound faith that ‘rural up-lift’ could only be sustained if farming communities were trained to ‘help themselves’ by investing physically and psychologically in the process of modernisation. We then locate subsequent attempts to incentivise and accelerate international agricultural development within the broader geopolitical imperatives of the Green Revolution and the Cold War. While US technical assistance undoubtedly sought to prevent political upheaval in the Third World, we argue that Rockefeller-led modernisation projects, based on insights gleaned from behavioural economics, championed a model of human capital – and the idea of ‘revolution within’ – in order to contain the threat of ‘revolution without’. Approaching agricultural development through this problematisation of the farmer reveals the ‘long history’ of the Green Revolution – unfolding from the domestic to the international and from the late nineteenth century to the present – as well as the continuing role of philanthropy in forging a new global order.A Philip Leverhulme Prize as well as Cambridge Humanities Research Grant awarded to Nally helped to support this research.This is the author accepted manuscript. The final version is available from Elsevier at http://dx.doi.org/10.1016/j.polgeo.2015.04.00

Comparative Proteomic Analysis of Differentially Expressed Proteins in the Urine of Reservoir Hosts of Leptospirosis

Rattus norvegicus is a natural reservoir host for pathogenic species of Leptospira. Experimentally infected rats remain clinically normal, yet persistently excrete large numbers of leptospires from colonized renal tubules via urine, despite a specific host immune response. Whilst persistent renal colonization and shedding is facilitated in part by differential antigen expression by leptospires to evade host immune responses, there is limited understanding of kidney and urinary proteins expressed by the host that facilitates such biological equilibrium. Urine pellets were collected from experimentally infected rats shedding leptospires and compared to urine from non-infected controls spiked with in vitro cultivated leptospires for analysis by 2-D DIGE. Differentially expressed host proteins include membrane metallo endopeptidase, napsin A aspartic peptidase, vacuolar H+ATPase, kidney aminopeptidase and immunoglobulin G and A. Loa22, a virulence factor of Leptospira, as well as the GroEL, were increased in leptospires excreted in urine compared to in vitro cultivated leptospires. Urinary IgG from infected rats was specific for leptospires. Results confirm differential protein expression by both host and pathogen during chronic disease and include markers of kidney function and immunoglobulin which are potential biomarkers of infection

A JWST/MIRI and NIRCam Analysis of the Young Stellar Object Population in the Spitzer I region of NGC 6822

We present an imaging survey of the Spitzer~I star-forming region in NGC 6822 conducted with the NIRCam and MIRI instruments onboard JWST. Located at a distance of 490 kpc, NGC 6822 is the nearest non-interacting low-metallicity ($\sim$0.2 $Z_{\odot}$) dwarf galaxy. It hosts some of the brightest known HII regions in the local universe, including recently discovered sites of highly-embedded active star formation. Of these, Spitzer I is the youngest and most active, and houses 90 color-selected candidate young stellar objects (YSOs) identified from Spitzer Space Telescope observations. We revisit the YSO population of Spitzer~I with these new JWST observations. By analyzing color-magnitude diagrams (CMDs) constructed with NIRCam and MIRI data, we establish color selection criteria and construct spectral energy distributions (SEDs) to identify candidate YSOs and characterize the full population of young stars, from the most embedded phase to the more evolved stages. In this way, we have identified 129 YSOs in Spitzer I. Comparing to previous Spitzer studies of the NGC 6822 YSO population, we find that the YSOs we identify are fainter and less massive, indicating that the improved resolution of JWST allows us to resolve previously blended sources into individual stars.Comment: 17 pages, 9 figures, 2 tables, to be submitted to ApJ, comments welcom

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

BACKGROUND: Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software "hier.part package" has been developed for running HP in R software. Its authors highlight a "minor rounding error" for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. METHODOLOGY/PRINCIPAL FINDINGS: Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. CONCLUSIONS/SIGNIFICANCE: HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Potent Innate Immune Response to Pathogenic Leptospira in Human Whole Blood

Background: Leptospirosis is caused by pathogenic spirochetes of the genus Leptospira. The bacteria enter the human body via abraded skin or mucous membranes and may disseminate throughout. In general the clinical picture is mild but some patients develop rapidly progressive, severe disease with a high case fatality rate. Not much is known about the innate immune response to leptospires during haematogenous dissemination. Previous work showed that a human THP-1 cell line recognized heat-killed leptospires and leptospiral LPS through TLR2 instead of TLR4. The LPS of virulent leptospires displayed a lower potency to trigger TNF production by THP-1 cells compared to LPS of non-virulent leptospires. Methodology/Principal Findings: We investigated the host response and killing of virulent and non-virulent Leptospira of different serovars by human THP-1 cells, human PBMC's and human whole blood. Virulence of each leptospiral strain was tested in a well accepted standard guinea pig model. Virulent leptospires displayed complement resistance in human serum and whole blood while in-vitro attenuated non-virulent leptospires were rapidly killed in a complement dependent manner. In vitro stimulation of THP-1 and PBMC's with heat-killed and living leptospires showed differential serovar and cell type dependence of cytokine induction. However, at low, physiological, leptospiral dose, living virulent complement resistant strains were consistently more potent in whole blood stimulations than the corresponding non-virulent complement sensitive strains. At higher dose living virulent and non-virulent leptospires were equipotent in whole blood. Inhibition of different TLRs indicated that both TLR2 and TLR4 as well as TLR5 play a role in the whole blood cytokine response to living leptospires. Conclusions/Significance: Thus, in a minimally altered system as human whole blood, highly virulent Leptospira are potent inducers of the cytokine response