Project description:The emerging field of omics - large-scale data-rich biological measurements of the genome - provides new opportunities to advance and strengthen research into endocrine-disrupting chemicals (EDCs). Although some EDCs have been associated with adverse health effects in humans, our understanding of their impact remains incomplete. Progress in the field has been primarily limited by our inability to adequately estimate and characterize exposure and identify sensitive and measurable outcomes during windows of vulnerability. Evolving omics technologies in genomics, epigenomics and mitochondriomics have the potential to generate data that enhance exposure assessment to include the exposome - the totality of the lifetime exposure burden - and provide biology-based estimates of individual risks. Applying omics technologies to expand our knowledge of individual risk and susceptibility will augment biological data in the prediction of variability and response to disease, thereby further advancing EDC research. Together, refined exposure characterization and enhanced disease-risk prediction will help to bridge crucial gaps in EDC research and create opportunities to move the field towards a new vision - precision public health.
Project description:Psychoneuroimmunology and immunopsychiatry are quickly approaching a critical point where the clinical translatability of their evidence base will be tested. To maximize chances for translational success, we believe researchers must adopt causal inference techniques that augment the causal relevance of estimates given theorized causal structures. To illustrate the utility of incorporating causal inference perspectives into psychoneuroimmunology, we applied directed acyclic graphs and a combination of empirical and simulated data to demonstrate the consequences of controlling for adiposity when testing the association between inflammation and depression under the plausible causal structure of increases in adipose tissue leading to greater inflammation that in turn promotes depression. Effect size estimates were pulled from a dataset combining the Midlife in the United States 2 (MIDUS-2) and MIDUS Refresher datasets. Data were extracted and used to simulate data reflecting an adiposity → inflammation → depression causal structure. Next, a Monte Carlo simulation study with 1,000 iterations and three sample size scenarios (Ns = 100, 250, and 500) was conducted testing whether controlling for adiposity when estimating the relation between inflammation and depression influenced the precision of this estimate. Across all simulation scenarios, controlling for adiposity reduced precision of the inflammation → depression estimate, suggesting that researchers primarily interested in quantifying inflammation → depression associations should not control for adiposity. This work thus underscores the importance of incorporating causal inference approaches into psychoneuroimmunological research.
Project description:Recent advances in our understanding of the neurobiology of tics have led to the development of novel rodent models capturing different pathophysiological and phenotypic aspects of Tourette syndrome. The proliferation of these models, however, raises vexing questions on what standards should be adopted to assess their theoretical validity and empirical utility. Assessing the homology of a rodent motoric burst with a tic remains problematic, due to our incomplete knowledge of the underpinnings of tics, their high phenotypic complexity and variability, limitations in our ability test key aspects of tic phenomenology (such as premonitory sensory phenomena) in animals, and between-species differences in neuroanatomy and behavioral repertoire. These limitations underscore that any interpretation of behavioral output in an animal model cannot exclusively rely on the recognition of features that bear superficial resemblance with tics, but must be supported by other etiological and convergent phenomenological criteria.Here, we discuss two complementary approaches for the study and validation of tic-like manifestations in rodents, based respectively on the use of contextual modulators and accompanying features of repetitive motor manifestations and on the reproduction of pathogenic factors.Neither strategy can by itself provide convincing evidence that a model informatively recapitulates tic pathophysiology. Their combination holds promise to enhance the rigorous evaluation and translational relevance of rodent models of tic disorders.This systematic consideration of different approaches to the validation and study of animal models of tic pathophysiology provides a framework for future work in this area.
Project description:Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.
Project description:Tumour hypoxia has been pursued as a cancer drug target for over 30 years, most notably using bioreductive (hypoxia-activated) prodrugs that target antineoplastic agents to low-oxygen tumour compartments. Despite compelling evidence linking hypoxia with treatment resistance and adverse prognosis, a number of such prodrugs have recently failed to demonstrate efficacy in pivotal clinical trials; an outcome that demands reflection on the discovery and development of these compounds. In this review, we discuss a clear disconnect between the pathobiology of tumour hypoxia, the pharmacology of hypoxia-activated prodrugs and the manner in which they have been taken into clinical development. Hypoxia-activated prodrugs have been evaluated in the manner of broad-spectrum cytotoxic agents, yet a growing body of evidence suggests that their activity is likely to be dependent on the coincidence of tumour hypoxia, expression of specific prodrug-activating reductases and intrinsic sensitivity of malignant clones to the cytotoxic effector. Hypoxia itself is highly variable between and within individual tumours and is not treatment-limiting in all cancer subtypes. Defining predictive biomarkers for hypoxia-activated prodrugs and overcoming the technical challenges of assaying them in clinical settings will be essential to deploying these agents in the era of personalised cancer medicine.
Project description:Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.
Project description:Long non-coding RNAs (lncRNAs) represent a major fraction of the transcriptome in multicellular organisms. Although a handful of well-studied lncRNAs are broadly recognized as biologically meaningful, the fraction of such transcripts out of the entire collection of lncRNAs remains a subject of vigorous debate. Here we review the evidence for and against biological functionalities of lncRNAs and attempt to arrive at potential modes of lncRNA functionality that would reconcile the contradictory conclusions. Finally, we discuss different strategies of phenotypic analyses that could be used to investigate such modes of lncRNA functionality.
Project description:We compute desorption rates for isolated polymers adsorbed to a solid wall with a rare event sampling technique called multilevel splitting, also known as forward flux sampling. We interpret computed rates with theories based on the conjecture that the product tdesDRg2 of the desorption time tdes and diffusivity D divided by squared radius of gyration Rg scales with exp(h/Rg) where h is the equilibrium ratio of adsorbed surface concentration of polymer Γ to bulk concentration of polymer c. As the polymer-wall interaction energy is increased, the slope of lntdesDRg2 vs. NVMFkBT nearly approaches unity, as expected for strongly-adsorbing chains, where N is the degree of polymerization and VMF is the height-averaged monomer-wall interaction energy for a strongly adsorbed chain. However, we also find that this scaling law is only accurate when adsorption strength per monomer exceeds a threshold value on the order of 0.3-0.5 kBT for a freely jointed chain without or with excluded volume effects. Below the critical value, we observe that tdesDRg2 becomes nearly constant with N, so that tdes∝Nα, with α≈2. This suggests a crossover from "strong" detachment-controlled to a "weak" diffusion-controlled desorption rate as VMF/kBT drops below some threshold. These results may partially explain experimental data, that in some cases show "strong" exponential dependence of desorption time on chain length, while in others a "weak" power-law dependence is found. However, in the "strong" adsorption case, our results suggest much longer desorption times than those measured, while the reverse is true in the weak adsorption limit. We discuss possible reasons for these discrepancies.