Ecological and methodological drivers of species' distribution and phenology responses to climate change

Climate change is shifting species’ distribution and phenology. Ecological traits, such as mobility or reproductive mode, explain variation in observed rates of shift for some taxa. However, estimates of relationships between traits and climate responses could be influenced by how responses are measured. We compiled a global data set of 651 published marine species’ responses to climate change, from 47 papers on distribution shifts and 32 papers on phenology change. We assessed the relative importance of two classes of predictors of the rate of change, ecological traits of the responding taxa and methodological approaches for quantifying biological responses. Methodological differences explained 22% of the variation in range shifts, more than the 7.8% of the variation explained by ecological traits. For phenology change, methodological approaches accounted for 4% of the variation in measurements, whereas 8% of the variation was explained by ecological traits. Our ability to predict responses from traits was hindered by poor representation of species from the tropics, where temperature isotherms are moving most rapidly. Thus, the mean rate of distribution change may be underestimated by this and other global syntheses. Our analyses indicate that methodological approaches should be explicitly considered when designing, analysing and comparing results among studies. To improve climate impact studies, we recommend that (1) reanalyses of existing time series state how the existing data sets may limit the inferences about possible climate responses; (2) qualitative comparisons of species’ responses across different studies be limited to studies with similar methodological approaches; (3) meta-analyses of climate responses include methodological attributes as covariates; and (4) that new time series be designed to include the detection of early warnings of change or ecologically relevant change. Greater consideration of methodological attributes will improve the accuracy of analyses that seek to quantify the role of climate change in species’ distribution and phenology changes

Strengthening confidence in climate change impact science

Aim: To assess confidence in conclusions about climate-driven biological change through time, and identify approaches for strengthening confidence scientific conclusions about ecological impacts of climate change. Location: Global. Methods: We outlined a framework for strengthening confidence in inferences drawn from biological climate impact studies through the systematic integration of prior expectations, long-term data and quantitative statistical procedures. We then developed a numerical confidence index (Cindex) and used it to evaluate current practices in 208 studies of marine climate impacts comprising 1735 biological time series. Results: Confidence scores for inferred climate impacts varied widely from 1 to 16 (very low to high confidence). Approximately 35% of analyses were not associated with clearly stated prior expectations and 65% of analyses did not test putative non-climate drivers of biological change. Among the highest-scoring studies, 91% tested prior expectations, 86% formulated expectations for alternative drivers but only 63% statistically tested them. Higher confidence scores observed in studies that did not detect a change or tracked multiple species suggest publication bias favouring impact studies that are consistent with climate change. The number of time series showing climate impacts was a poor predictor of average confidence scores for a given group, reinforcing that vote-counting methodology is not appropriate for determining overall confidence in inferences. Main conclusions: Climate impacts research is expected to attribute biological change to climate change with measurable confidence. Studies with long-term, high-resolution data, appropriate statistics and tests of alternative drivers earn higher Cindex scores, suggesting these should be given greater weight in impact assessments. Together with our proposed framework, the results of our Cindex analysis indicate how the science of detecting and attributing biological impacts to climate change can be strengthened through the use of evidence-based prior expectations and thorough statistical analyses, even when data are limited, maximizing the impact of the diverse and growing climate change ecology literature

Predicting functional associations from metabolism using bi-partite network algorithms

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.</p> <p>Results</p> <p>We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.</p> <p>Conclusions</p> <p>We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.</p

Economic Impact of Dengue Illness and the Cost-Effectiveness of Future Vaccination Programs in Singapore

Dengue illness is a tropical disease transmitted by mosquitoes that threatens more than one third of the worldwide population. Dengue has important economic consequences because of the burden to hospitals, work absenteeism and risk of death of symptomatic cases. Governments attempt to reduce the disease burden using costly mosquito control strategies such as habitat reduction and spraying insecticide. Despite such efforts, the number of cases remains high. Dengue vaccines are expected to be available in the near future and there is an urgent need to evaluate their cost-effectiveness, i.e. whether their cost will be justified by the reduction in disease burden they bring. For such an evaluation, we estimated the economic impacts of dengue in Singapore and the expected vaccine costs for different prices. In this way we estimated price thresholds for which vaccination is not cost-effective. This research provides useful estimates that will contribute to informed decisions regarding the adoption of dengue vaccination programs

Mitoxantrone Induces Natural Killer Cell Maturation in Patients with Secondary Progressive Multiple Sclerosis

Mitoxantrone is one of the few drugs approved for the treatment of progressive multiple sclerosis (MS). However, the prolonged use of this potent immunosuppressive agent is limited by the appearance of severe side effects. Apart from its general cytotoxic effect, the mode of action of mitoxantrone on the immune system is poorly understood. Thus, to develop safe therapeutic approaches for patients with progressive MS, it is essential to elucidate how mitoxantrone exerts it benefits. Accordingly, we initiated a prospective single-arm open-label study with 19 secondary progressive MS patients. We investigated long-term effects of mitoxantrone on patient peripheral immune subsets using flow cytometry. While we corroborate that mitoxantrone persistently suppresses B cells in vivo, we show for the first time that treatment led to an enrichment of neutrophils and immunomodulatory CD8low T cells. Moreover, sustained mitoxantrone applications promoted not only persistent NK cell enrichment but also NK cell maturation. Importantly, this mitoxantrone-induced NK cell maturation was seen only in patients that showed a clinical response to treatment. Our data emphasize the complex immunomodulatory role of mitoxantrone, which may account for its benefit in MS. In particular, these results highlight the contribution of NK cells to mitoxantrone efficacy in progressive MS

Timeless standards for species delimitation

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Testing and Assessment in an International Context: Cross- and Multi-cultural Issues

Globalisation, increase of migration flows, and the concurrent worldwide competitiveness impose rethinking of testing and assessment procedures and practices in an international and multicultural context. This chapter reviews the methodological and practical implications for psychological assessment in the field of career guidance. The methodological implications are numerous and several aspects have to be considered, such as cross-cultural equivalence or construct, method, and item bias. Moreover, the construct of culture by itself is difficult to define and difficult to measure. In order to provide non-discriminatory assessment, counsellors should develop their clinical cross-cultural competencies, develop more specific intervention strategies, and respect cultural differences. Several suggestions are given concerning translation and adaptation of psychological instruments, developing culture specific measures, and the use of these instruments. More research in this field should use mixed methods, multi-centric designs, and consider emic and etic psychological variables. A multidisciplinary approach might also allow identifying culture specific and ecological meaningful constructs. Non-discriminatory assessment implies considering the influence and interaction of personal characteristics and environmental factors

Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.</p> <p>Results</p> <p>In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.</p> <p>Conclusions</p> <p>The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.</p