Civil society organisations and Target 4.7 of the SDGs: towards intersectionality for promoting a more just and sustainable world

Target 4.7 of the United Nations Sustainable Development Goals can provide an opportunity for a more transformative approach to education. To consider this requires a new approach to learning that moves beyond subjects and disciplines to recognise intersectionality as being central to providing a radical rethinking of the purpose of education. Evidence from initiatives by civil society organisations in England shows that there are examples of these practices. At an international level, the Dublin Declaration on Global Education to 2050 provides an additional boost to calling for a major transformation in education

The Use of ROC Analysis for the Qualitative Prediction of Human Oral Bioavailability from Animal Data

PURPOSE: To develop and evaluate a tool for the qualitative prediction of human oral bioavailability (F(human)) from animal oral bioavailability (F(animal)) data employing ROC analysis and to identify the optimal thresholds for such predictions. METHODS: A dataset of 184 compounds with known F(human) and F(animal) in at least one species (mouse, rat, dog and non-human primates (NHP)) was employed. A binary classification model for F(human) was built by setting a threshold for high/low F(human) at 50%. The thresholds for high/low F(animal) were varied from 0 to 100 to generate the ROC curves. Optimal thresholds were derived from ‘cost analysis’ and the outcomes with respect to false negative and false positive predictions were analyzed against the BDDCS class distributions. RESULTS: We successfully built ROC curves for the combined dataset and per individual species. Optimal F(animal) thresholds were found to be 67% (mouse), 22% (rat), 58% (dog), 35% (NHP) and 47% (combined dataset). No significant trends were observed when sub-categorizing the outcomes by the BDDCS. CONCLUSIONS: F(animal) can predict high/low F(human) with adequate sensitivity and specificity. This methodology and associated thresholds can be employed as part of decisions related to planning necessary studies during development of new drug candidates and lead selection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-013-1193-2) contains supplementary material, which is available to authorized users

Targeted knock-down of miR21 primary transcripts using snoMEN vectors induces apoptosis in human cancer cell lines

We have previously reported an antisense technology, 'snoMEN vectors', for targeted knock-down of protein coding mRNAs using human snoRNAs manipulated to contain short regions of sequence complementarity with the mRNA target. Here we characterise the use of snoMEN vectors to target the knock-down of micro RNA primary transcripts. We document the specific knock-down of miR21 in HeLa cells using plasmid vectors expressing miR21-targeted snoMEN RNAs and show this induces apoptosis. Knock-down is dependent on the presence of complementary sequences in the snoMEN vector and the induction of apoptosis can be suppressed by over-expression of miR21. Furthermore, we have also developed lentiviral vectors for delivery of snoMEN RNAs and show this increases the efficiency of vector transduction in many human cell lines that are difficult to transfect with plasmid vectors. Transduction of lentiviral vectors expressing snoMEN targeted to pri-miR21 induces apoptosis in human lung adenocarcinoma cells, which express high levels of miR21, but not in human primary cells. We show that snoMEN-mediated suppression of miRNA expression is prevented by siRNA knock-down of Ago2, but not by knock-down of Ago1 or Upf1. snoMEN RNAs colocalise with Ago2 in cell nuclei and nucleoli and can be co-immunoprecipitated from nuclear extracts by antibodies specific for Ago2

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives

BACKGROUND: Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. RESULTS: Here we report a new implementation of the CM method with an added set of selection rules (filters). The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR) of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. CONCLUSION: Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known