Potential hotspots identified by social LCA—part 1: a case study of a laptop computer

Purpose

A generic hotspot assessment of social impacts from a product was conducted, using a laptop computer as a case. The aims of the case study were to identify social hotspots of the laptop and to test and evaluate the methodology.

Methods

The case study was based on the social LCA methodology described in the Guidelines for social LCA and included the product system from ‘cradle to grave’ as well as the impacts on all relevant stakeholders. We focused on a simplified list of materials and used mainly country-specific data.

Results and discussion

A new method for impact assessment of hotspots was developed. The total activity in each phase was distributed among countries. The countries were divided into groups related to the extent of activity in the product system, as well as to their performance on a subcategory. High values in both groups were highlighted and hotspots were identified. The results revealed some hotspots, some hot countries and some hot issues, all indicating a risk of negative social impacts in the product system of a laptop. It also identified workers and the local community as the stakeholders most at risk of negative social impacts. Among the hotspots identified, the following subcategories were of importance: safe and healthy living conditions, social benefit/social security, access to material resources, involvement in areas with armed conflicts, community engagement (lack of), corruption, and access to immaterial resources.

Conclusions

The study showed it is possible to conduct a social LCA on a generic complex product using the Guidelines, even though data collection was impaired by lack of data and low data quality. It identified methodological issues that need further attention, for example the indicator impact pathways. Still, it is clear that new insights can be gained by social LCA, where the life cycle perspective and the systematic approach help users identify potentially important aspects that could otherwise have been neglected.     

Implementing academic detailing for breast cancer screening in underserved communities

Background

Canadian funding agencies are no longer content to support research that solely advances scientific knowledge, and key directives are now in place to promote research transfer to policy- and decision-makers. Therefore, it is necessary to improve our understanding of how researchers are trained and supported to facilitate knowledge translation activities. In this study, we investigated differences in health researcher characteristics and knowledge translation activities.

Methods

Our sample consisted of 240 health researchers from three Alberta universities. Respondents were classified by research domain [basic (n = 72) or applied (n = 168)] and faculty [medical school (n = 128) or other health science (n = 112)]. We examined our findings using Mode I and Mode II archetypes of knowledge production, which allowed us to consider the scholarly and social contexts of knowledge production and translation.

Results

Differences among health researcher professional characteristics were not statistically significant. There was a significant gender difference in the applied researcher faculty group, which was predominantly female (p <.05). Research domain was linked to translation activities. Applied researchers reported engaging in significantly more Mode II activities than basic researchers (p <.001), and scored higher than basic researchers regarding the perceived importance of translation activities (Mode I, p =.01; Mode II, p <.001). Main effects of faculty were limited to engaged dissemination (medical school < other faculties; p =.025) and number of publications (medical school > other faculties; p =.004). There was an interaction effect for research domain and faculty group for number of publications (p =.01), in that applied researchers in medical faculties published more than their peers in other faculty groups.

Conclusion

Our findings illustrate important differences between health researchers and provide beginning insights into their professional characteristics and engagement in Mode I and Mode II activities. A future study designed to examine these dimensions in greater detail, including potential covariates across more varied institutions, would yield richer insights and enable an examination of relative influences, needs and costs of each mode of activity.     

A novel tool for individual haplotype inference using mixed data

Background

In many studies, researchers may recruit samples consisting of independent trios and unrelated individuals. However, most of the currently available haplotype inference methods do not cope well with these kinds of mixed data sets.

Methods

We propose a general and simple methodology using a mixture of weighted multinomial (MIXMUL) approach that combines separate haplotype information from unrelated individuals and independent trios for haplotype inference to the individual level.

Results

The new MIXMUL procedure improves over existing methods in that it can accurately estimate haplotype frequencies from mixed data sets and output probable haplotype pairs in optimized reconstruction outcomes for all subjects that have contributed to estimation. Simulation results showed that this new MIXMUL procedure competes well with the EM-based method, i.e. FAMHAP, under a few assumed scenarios.

Conclusion

The results showed that MIXMUL can provide accurate estimates similar to those haplotype frequencies obtained from FAMHAP and output the probable haplotype pairs in the most optimal reconstruction outcome for all subjects that have contributed to estimation. If available data consist of combinations of unrelated individuals and independent trios, the MIXMUL procedure can be used to estimate the haplotype frequencies accurately and output the most likely reconstructed haplotype pairs of each subject in the estimation.     

Formation of VEGF isoform-specific spatial distributions governing angiogenesis: computational analysis

Background

Cellular interaction networks can be used to analyze the effects on cell signaling and other functional consequences of perturbations to cellular physiology. Thus, several methods have been used to reconstitute interaction networks from multiple published datasets. However, the structure and performance of these networks depends on both the quality and the unbiased nature of the original data. Due to the inherent bias against membrane proteins in protein-protein interaction (PPI) data, interaction networks can be compromised particularly if they are to be used in conjunction with drug screening efforts, since most drug-targets are membrane proteins.

Results

To overcome the experimental bias against PPIs involving membrane-associated proteins we used a probabilistic approach based on a hypergeometric distribution followed by logistic regression to simultaneously optimize the weights of different sources of interaction data. The resulting less biased genome-scale network constructed for the budding yeast Saccharomyces cerevisiae revealed that the starvation pathway is a distinct subnetwork of autophagy and retrieved a more integrated network of unfolded protein response genes. We also observed that the centrality-lethality rule depends on the content of membrane proteins in networks.

Conclusions

We show here that the bias against membrane proteins can and should be corrected in order to have a better representation of the interactions and topological properties of protein interaction networks.     

Minor differences in haplotype frequency estimates can produce very large differences in heterogeneity test statistics

Background

Genome wide association (GWA) studies provide the opportunity to develop new kinds of analysis. Analysing pairs of markers from separate regions might lead to the detection of allelic association which might indicate an interaction between nearby genes.

Methods

396,591 markers typed in 541 subjects were studied. 7.8*10¹⁰ pairs of markers were screened and those showing initial evidence for allelic association were subjected to more thorough investigation along with 10 flanking markers on either side.

Results

No evidence was detected for interaction. However 6 markers appeared to have an incorrect map position according to NCBI Build 35. One of these was corrected in Build 36 and 2 were dropped. The remaining 3 were left with map positions inconsistent with their allelic association relationships.

Discussion

Although no interaction effects were detected the method was successful in identifying markers with probably incorrect map positions.

Conclusion

The study of allelic association can supplement other methods for assigning markers to particular map positions. Analyses of this type may usefully be applied to data from future GWA studies.     

Identifying Social Learning in Animal Populations: A New ‘Option-Bias’ Method

Background

Studies of natural animal populations reveal widespread evidence for the diffusion of novel behaviour patterns, and for intra- and inter-population variation in behaviour. However, claims that these are manifestations of animal ‘culture’ remain controversial because alternative explanations to social learning remain difficult to refute. This inability to identify social learning in social settings has also contributed to the failure to test evolutionary hypotheses concerning the social learning strategies that animals deploy.

Methodology/Principal Findings

We present a solution to this problem, in the form of a new means of identifying social learning in animal populations. The method is based on the well-established premise of social learning research, that - when ecological and genetic differences are accounted for - social learning will generate greater homogeneity in behaviour between animals than expected in its absence. Our procedure compares the observed level of homogeneity to a sampling distribution generated utilizing randomization and other procedures, allowing claims of social learning to be evaluated according to consensual standards. We illustrate the method on data from groups of monkeys provided with novel two-option extractive foraging tasks, demonstrating that social learning can indeed be distinguished from unlearned processes and asocial learning, and revealing that the monkeys only employed social learning for the more difficult tasks. The method is further validated against published datasets and through simulation, and exhibits higher statistical power than conventional inferential statistics.

Conclusions/Significance

The method is potentially a significant technological development, which could prove of considerable value in assessing the validity of claims for culturally transmitted behaviour in animal groups. It will also be of value in enabling investigation of the social learning strategies deployed in captive and natural animal populations.     

Effective generation of transgenic pigs and mice by linker based sperm-mediated gene transfer.

Background

Transgenic animals have become valuable tools for both research and applied purposes. The current method of gene transfer, microinjection, which is widely used in transgenic mouse production, has only had limited success in producing transgenic animals of larger or higher species. Here, we report a linker based sperm-mediated gene transfer method (LB-SMGT) that greatly improves the production efficiency of large transgenic animals.

Results

The linker protein, a monoclonal antibody (mAb C), is reactive to a surface antigen on sperm of all tested species including pig, mouse, chicken, cow, goat, sheep, and human. mAb C is a basic protein that binds to DNA through ionic interaction allowing exogenous DNA to be linked specifically to sperm. After fertilization of the egg, the DNA is shown to be successfully integrated into the genome of viable pig and mouse offspring with germ-line transfer to the F1 generation at a highly efficient rate: 37.5% of pigs and 33% of mice. The integration is demonstrated again by FISH analysis and F2 transmission in pigs. Furthermore, expression of the transgene is demonstrated in 61% (35/57) of transgenic pigs (F0 generation).

Conclusions

Our data suggests that LB-SMGT could be used to generate transgenic animals efficiently in many different species.     

Predicting active site residue annotations in the Pfam database

Background

The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction.

Results

This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast.

Conclusion

Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.     

Optimized submerged batch fermentation strategy for systems scale studies of metabolic switching in Streptomyces coelicolor A3(2)

Background

Given the complex mechanisms underlying biochemical processes systems biology researchers tend to build ever increasing computational models. However, dealing with complex systems entails a variety of problems, e.g. difficult intuitive understanding, variety of time scales or non-identifiable parameters. Therefore, methods are needed that, at least semi-automatically, help to elucidate how the complexity of a model can be reduced such that important behavior is maintained and the predictive capacity of the model is increased. The results should be easily accessible and interpretable. In the best case such methods may also provide insight into fundamental biochemical mechanisms.

Results

We have developed a strategy based on the Computational Singular Perturbation (CSP) method which can be used to perform a "biochemically-driven" model reduction of even large and complex kinetic ODE systems. We provide an implementation of the original CSP algorithm in COPASI (a COmplex PAthway SImulator) and applied the strategy to two example models of different degree of complexity - a simple one-enzyme system and a full-scale model of yeast glycolysis.

Conclusion

The results show the usefulness of the method for model simplification purposes as well as for analyzing fundamental biochemical mechanisms. COPASI is freely available at http://www.copasi.org.     

Swift block-updating EM and pseudo-EM procedures for Bayesian shrinkage analysis of quantitative trait loci

Introduction

Virtually all existing expectation-maximization (EM) algorithms for quantitative trait locus (QTL) mapping overlook the covariance structure of genetic effects, even though this information can help enhance the robustness of model-based inferences.

Results

Here, we propose fast EM and pseudo-EM-based procedures for Bayesian shrinkage analysis of QTLs, designed to accommodate the posterior covariance structure of genetic effects through a block-updating scheme. That is, updating all genetic effects simultaneously through many cycles of iterations.

Conclusion

Simulation results based on computer-generated and real-world marker data demonstrated the ability of our method to swiftly produce sensible results regarding the phenotype-to-genotype association. Our new method provides a robust and remarkably fast alternative to full Bayesian estimation in high-dimensional models where the computational burden associated with Markov chain Monte Carlo simulation is often unwieldy. The R code used to fit the model to the data is provided in the online supplementary material.     

Molecular genetics research in sub-Saharan Africa: how can the international community help?

Background

Opportunities provided by rapidly increasing access to educational resources, clinical and epidemiological data, DNA collections, cheaper technology and financial investment, suggest that researchers in sub-Saharan Africa outside South Africa (SSAOSA) could now join the genomics revolution on equal terms with those in the West.

Findings

Current evidence, however, suggests that, in some cases, various factors may be compromising this development. One interpretation is that urgent practical problems, which may compromise motivation, aspiration and ambition, are blocking opportunity.

Conclusions

Those wishing to help should support the SSAOSA scientists both at the level of extending collaboration networks and in stimulating academic leadership at national and institutional levels to ensure adequate resources are allocated. Members of organisations representing the international community of human geneticists, such as HUGO, have a significant responsibility in supporting such activities.     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

Comparative performance of the 16S rRNA gene in DNA barcoding of amphibians

Background

Hybridization receives attention because of the potential role that it may play in generating evolutionary novelty. An explanation for the emergence of novel phenotypes is given by transgressive segregation, which, if frequent, would imply an important evolutionary role for hybridization. This process is still rarely studied in natural populations as samples of recent hybrids and their parental populations are needed. Further, the detection of transgressive segregation requires phenotypes that can be easily quantified and analysed. We analyse variability in body shape of divergent populations of European sculpins (Cottus gobio complex) as well as natural hybrids among them.

Results

A distance-based method is developed to assign unknown specimens to known groups based on morphometric data. Apparently, body shape represents a highly informative set of characters that parallels the discriminatory power of microsatellite markers in our study system. Populations of sculpins are distinct and "unknown" specimens can be correctly assigned to their source population based on body shape. Recent hybrids are intermediate along the axes separating their parental groups but display additional differentiation that is unique and coupled with the hybrid genetic background.

Conclusion

There is a specific hybrid shape component in natural sculpin hybrids that can be best explained by transgressive segregation. This inference of how hybrids differ from their ancestors provides basic information for future evolutionary studies. Furthermore, our approach may serve to assign candidate specimens to their source populations based on morphometric data and help in the interpretation of population differentiation.     

Opening the Black Box of Developmental Experiments: Behavioural Mechanisms Underlying Long‐Term Effects of Early Social Experience

Typically, animals spend a considerable portion of their time with social interactions involving mates, offspring, competitors and group members. The social performance during these interactions can strongly depend on the social environment individuals have experienced early in life. Despite a considerable number of experiments investigating long‐term effects of the early social environment, our understanding of the behavioural mechanisms mediating these effects is still limited, mainly for two reasons. (1) Only in few experimental studies have researchers actually observed and quantified the behaviour of their study animals during the social treatment. (2) Even if differences in social interactions between social rearing treatments are reported, these differences might not be causally linked to any observed long‐term effects later in life. The aim of this review was to investigate whether behavioural records of animals during the experimental manipulation of their social environment can help (1) identifying behavioural mechanisms involved in a long‐term effect and (2) obtaining a better understanding of the long‐term consequences of early manipulations. First, I review studies that manipulated the social environment at an early stage of the ontogeny, observed the social interactions and behaviour during the social experience phase and subsequently tested the performance in social and non‐social behavioural tasks at a later life stage. In all reviewed studies, treatment differences were reported both in social interactions during the social experience phase and in social and/or non‐social behaviours later in life. Second, I discuss four classes of behavioural mechanisms that can cause the reported long‐term effects of social experience, namely learning by experience, social learning, sensory stimulation and social cueing. I conclude that social interactions during the social experience phase should always be recorded for at least two reasons. Knowledge about how the social interactions differ between rearing treatments (1) permits researchers to formulate hypotheses about candidate mechanisms causing long‐term effects on behaviour and (2) can help to interpret unexpected outcomes of developmental experiments. Finally, I propose that as a crucial ultimate step towards understanding effects of the early social environment, we should develop targeted experiments testing for the causality of identified candidate mechanism.     

Hospital staff education on severe sepsis/septic shock and hospital mortality: an original hypothesis

Background

The Functional Comorbidity Index (FCI) was recently developed to predict physical function in acute lung injury patients using comorbidity data. Our objectives were to determine: (1) the inter-rater reliability of the FCI collected using in-patient discharge summaries (primary objective); and (2) the accuracy and predictive validity of the FCI collected using hospital discharge summaries and admission records versus complete chart review (secondary objectives).

Methods

For reliability, we evaluated the FCI’s intraclass correlation coefficient (ICC) among trained research staff performing data collection for 421 acute lung injury patients enrolled in a prospective cohort study. For validity and accuracy, we compared the detection of FCI comorbidities across three types of inpatient medical records, and the association of the respective FCI scores obtained with patients’ SF-36 physical function subscale (PFS) scores at 1-year follow-up.

Results

Inter-rater reliability was near-perfect (ICC 0.91; 95% CI 0.89-0.94). Hospital admission records and discharge summaries (vs. complete chart review) significantly underestimated the total FCI score. However, using multivariable linear regression, FCI scores collected using each of the three types of inpatient medical records had similar associations with PFS, suggesting similar predictive value.

Conclusions

Data collection using in-patient discharge summaries represents a reliable and valid method for collecting FCI comorbidity information.