Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods

Active rehabilitation for chronic low back pain: Cognitive-behavioral, physical, or both? First direct post-treatment results from a randomized controlled trial [ISRCTN22714229]

BACKGROUND: The treatment of non-specific chronic low back pain is often based on three different models regarding the development and maintenance of pain and especially functional limitations: the deconditioning model, the cognitive behavioral model and the biopsychosocial model. There is evidence that rehabilitation of patients with chronic low back pain is more effective than no treatment, but information is lacking about the differential effectiveness of different kinds of rehabilitation. A direct comparison of a physical, a cognitive-behavioral treatment and a combination of both has never been carried out so far. METHODS: The effectiveness of active physical, cognitive-behavioral and combined treatment for chronic non-specific low back pain compared with a waiting list control group was determined by performing a randomized controlled trial in three rehabilitation centers. Two hundred and twenty three patients were randomized, using concealed block randomization to one of the following treatments, which they attended three times a week for 10 weeks: Active Physical Treatment (APT), Cognitive-Behavioral Treatment (CBT), Combined Treatment of APT and CBT (CT), or Waiting List (WL). The outcome variables were self-reported functional limitations, patient's main complaints, pain, mood, self-rated treatment effectiveness, treatment satisfaction and physical performance including walking, standing up, reaching forward, stair climbing and lifting. Assessments were carried out by blinded research assistants at baseline and immediately post-treatment. The data were analyzed using the intention-to-treat principle. RESULTS: For 212 patients, data were available for analysis. After treatment, significant reductions were observed in functional limitations, patient's main complaints and pain intensity for all three active treatments compared to the WL. Also, the self-rated treatment effectiveness and satisfaction appeared to be higher in the three active treatments. Several physical performance tasks improved in APT and CT but not in CBT. No clinically relevant differences were found between the CT and APT, or between CT and CBT. CONCLUSION: All three active treatments were effective in comparison to no treatment, but no clinically relevant differences between the combined and the single component treatments were found

Health-Related Quality of Life in Patients with Spinal Cord Injury: Review of the Short Form 36-Health Questionnaire Survey

Advances in medical and rehabilitative care have increased interest in studying how different factors may affect the health-related quality of life (HRQOL) of individuals with spinal cord injury (SCI). There has been a large increase in the number of studies assessing HRQOL among persons with SCI. However, despite these advances, numerous issues remain unanswered because these studies have used a variety of methodologic approaches and assessment tools to examine how different factors have a role in predicting HRQOL in SCI populations. Therefore, standardized instruments should be used as part of this process. The Medical Outcomes Study Short Form (SF-36) was developed in order to survey health status of the general population. However, the available data on the HRQOL of individuals with SCI are currently limited. In addition, there is little information currently available on the factors that are associated with HRQOL in the SCI population and this issue remains controversial. The findings from several individual studies that used the SF-36 to assess the HRQOL of patients suffering from SCI were reviewed, and the results were interpreted with disability in mind. This review article aims to summarize the data regarding the HRQOL of individuals with SCI by using the SF-36

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

HIF-driven SF3B1 induces KHK-C to enforce fructolysis and heart disease.

Fructose is a major component of dietary sugar and its overconsumption exacerbates key pathological features of metabolic syndrome. The central fructose-metabolising enzyme is ketohexokinase (KHK), which exists in two isoforms: KHK-A and KHK-C, generated through mutually exclusive alternative splicing of KHK pre-mRNAs. KHK-C displays superior affinity for fructose compared with KHK-A and is produced primarily in the liver, thus restricting fructose metabolism almost exclusively to this organ. Here we show that myocardial hypoxia actuates fructose metabolism in human and mouse models of pathological cardiac hypertrophy through hypoxia-inducible factor 1α (HIF1α) activation of SF3B1 and SF3B1-mediated splice switching of KHK-A to KHK-C. Heart-specific depletion of SF3B1 or genetic ablation of Khk, but not Khk-A alone, in mice, suppresses pathological stress-induced fructose metabolism, growth and contractile dysfunction, thus defining signalling components and molecular underpinnings of a fructose metabolism regulatory system crucial for pathological growth

Local deformation in a hydrogel induced by an external magnetic field

The aim of this study is to prove the feasibility of a system able to apply local mechanical loading on cells seeded in a hydrogel for tissue engineering applications. This experimental study is based on a previously developed artificial cartilage model with different concentrations of poly(vinyl alcohol) (PVA) that simulates the cartilage extracellular matrix (ECM). Poly(l-lactic acid) (PLLA) microspheres with dispersed magnetic nanoparticles (MNPs) were produced with an emulsion method. These microspheres were embedded in aqueous PVA solutions with varying concentration to resemble increased viscosity of growing tissue during regeneration. The ability to induce a local deformation in the ECM was assessed by applying a steady or an oscillatory magnetic field gradient to different PVA solutions containing the magnetic microparticles, similarly as in ferrogels. PLLA microparticle motion was recorded, and the images were analyzed. Besides, PVA gels and PLLA microparticles were introduced into the pores of a polycaprolactone scaffold, and the microparticle distribution and the mechanical properties of the construct were evaluated. The results of this experimental model show that the dispersion of PLLA microparticles containing MNPs, together with cells in a supporting gel, will allow applying local mechanical stimuli to cells during tissue regeneration. This local stimulation can have a positive effect on the differentiation of seeded cells and improve tissue regeneration.The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through the MAT2013-46467-C4-1-R project, including the Feder funds. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program. CIBER Actions are financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. 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Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential

Forest carbon sequestration:the impact of forest management

In this chapter, we describe alternative ways in which forests and forestry can help to mítigate climate change, along with the potential impact of these activities. The three carbon storage compartments should be considered inall impact estimates. Carbon content in living biomass is easily estimated via species-specific equations or by applying factors to oven-dry biomass weights (e.g.,lbañez et al.,2002, Herrero et al.,2011,Castaño and Bravo, 2012).Litter carbon content has been analysed in many studies on primary forest productivity, though information regarding the influence of forest management on litter carbon content is less abundant (Blanco et al., 2006). In the last decade,efforts have been made to assess soil carbon in forests, but studies on the effect of forest management on soils show discrepancies (Lindner and Karjalainen,2007).Hoover (2011), for example,found no difference in forest floor carbon stocks among stands subjected to partial or complete harvest treatments in the United States.Instituto Universitario de Gestión Forestal Sostenibl