Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction

This is the final version. Available from the American Geophysical Union via the DOI in this record. Using optimal detection techniques with climate model simulations, most of the observed increase of near-surface temperatures over the second half of the twentieth century is attributed to anthropogenic influences. However, the partitioning of the anthropogenic influence to individual factors, such as greenhouse gases and aerosols, is much less robust. Differences in how forcing factors are applied, in their radiative influence and in models’ climate sensitivities, substantially influence the response patterns. We find that standard optimal detection methodologies cannot fully reconcile this response diversity. By selecting a set of experiments to enable the diagnosing of greenhouse gases and the combined influence of other anthropogenic and natural factors, we find robust detections of well-mixed greenhouse gases across a large ensemble of models. Of the observed warming over the twentieth century of 0.65 K/century we find, using a multimodel mean not incorporating pattern uncertainty, a well-mixed greenhouse gas warming of 0.87 to 1.22 K/century. This is partially offset by cooling from other anthropogenic and natural influences of-0.54 to-0.22 K/century. Although better constrained than recent studies, the attributable trends across climate models are still wide, with implications for observational constrained estimates of transient climate response. Some of the uncertainties could be reduced in future by having more model data to better quantify the simulated estimates of the signals and natural variability, by designing model experiments more effectively and better quantification of the climate model radiative influences. Most importantly, how model pattern uncertainties are incorporated into the optimal detection methodology should be improved.Joint UK DECC/Defra Met Office Hadley Centre Climate Programm

Associations between sporting physical activity and cognition in mid and later-life: Evidence from two cohorts

Evidence has linked sporting leisure time physical activity (sporting-LTPA) to healthy cognition throughout adulthood. This may be due to the physiological effects of physical activity (PA), or to other, psychosocial facets of sport. We examined associations between sporting-LTPA and cognition while adjusting for device-measured PA volume devoid of context, both in midlife (N = 4041) participants from the 1970 British Cohort Study and later-life (N = 957) participants from the British Regional Heart Study. Independent of device-measured PA, we identified positive associations between sporting-LTPA and cognition. Sports with team/partner elements were strongly positively associated with cognition, suggesting LTPA context may be critical to this relationship

Super-resolving phase measurements with a multi-photon entangled state

Using a linear optical elements and post-selection, we construct an entangled polarization state of three photons in the same spatial mode. This state is analogous to a ``photon-number path entangled state'' and can be used for super-resolving interferometry. Measuring a birefringent phase shift, we demonstrate two- and three-fold improvements in phase resolution.Comment: 4 pages, 3 figure

An evaluation of enteral nutrition practices and nutritional provision in children during the entire length of stay in critical care

<b>Background</b> Provision of optimal nutrition in children in critical care is often challenging. This study evaluated exclusive enteral nutrition (EN) provision practices and explored predictors of energy intake and delay of EN advancement in critically ill children.<p></p> <b>Methods</b> Data on intake and EN practices were collected on a daily basis and compared against predefined targets and dietary reference values in a paediatric intensive care unit. Factors associated with intake and advancement of EN were explored.<p></p> <b>Results</b> Data were collected from 130 patients and 887 nutritional support days (NSDs). Delay to initiate EN was longer in patients from both the General Surgical and congenital heart defect (CHD) Surgical groups [Median (IQR); CHD Surgical group: 20.3 (16.4) vs General Surgical group: 11.4 (53.5) vs Medical group: 6.5 (10.9) hours; p <= 0.001]. Daily fasting time per patient was significantly longer in patients from the General Surgical and CHD Surgical groups than those from the Medical group [% of 24 h, Median (IQR); CHD Surgical group: 24.0 (29.2) vs General Surgical group: 41.7 (66.7) vs Medical group: 9.4 (21.9); p <= 0.001]. A lower proportion of fluids was delivered as EN per patient (45% vs 73%) or per NSD (56% vs 73%) in those from the CHD Surgical group compared with those with medical conditions. Protein and energy requirements were achieved in 38% and 33% of the NSDs. In a substantial proportion of NSDs, minimum micronutrient recommendations were not met particularly in those patients from the CHD Surgical group. A higher delivery of fluid requirements (p < 0.05) and a greater proportion of these delivered as EN (p < 0.001) were associated with median energy intake during stay and delay of EN advancement. Fasting (31%), fluid restriction (39%) for clinical reasons, procedures requiring feed cessation and establishing EN (22%) were the most common reasons why target energy requirements were not met.<p></p> <b>Conclusions</b> Provision of optimal EN support remains challenging and varies during hospitalisation and among patients. Delivery of EN should be prioritized over other "non-nutritional" fluids whenever this is possible.<p></p&gt

FHL1 reduces dystrophy in transgenic mice overexpressing FSHD muscular dystrophy region gene 1 (FRG1)

Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal-dominant disease with no effective treatment. The genetic cause of FSHD is complex and the primary pathogenic insult underlying the muscle disease is unknown. Several disease candidate genes have been proposed including DUX4 and FRG1. Expression analysis studies of FSHD report the deregulation of genes which mediate myoblast differentiation and fusion. Transgenic mice overexpressing FRG1 recapitulate the FSHD muscular dystrophy phenotype. Our current study selectively examines how increased expression of FRG1 may contribute to myoblast differentiation defects. We generated stable C2C12 cell lines overexpressing FRG1, which exhibited a myoblast fusion defect upon differentiation. To determine if myoblast fusion defects contribute to the FRG1 mouse dystrophic phenotype, this strain was crossed with skeletal muscle specific FHL1-transgenic mice. We previously reported that FHL1 promotes myoblast fusion in vitro and FHL1-transgenic mice develop skeletal muscle hypertrophy. In the current study, FRG1 mice overexpressing FHL1 showed an improvement in the dystrophic phenotype, including a reduced spinal kyphosis, increased muscle mass and myofiber size, and decreased muscle fibrosis. FHL1 expression in FRG1 mice, did not alter satellite cell number or activation, but enhanced myoblast fusion. Primary myoblasts isolated from FRG1 mice showed a myoblast fusion defect that was rescued by FHL1 expression. Therefore, increased FRG1 expression may contribute to a muscular dystrophy phenotype resembling FSHD by impairing myoblast fusion, a defect that can be rescued by enhanced myoblast fusion via expression of FHL1

Automated Analysis of Cryptococcal Macrophage Parasitism Using GFP-Tagged Cryptococci

The human fungal pathogens Cryptococcus neoformans and C. gattii cause life-threatening infections of the central nervous system. One of the major characteristics of cryptococcal disease is the ability of the pathogen to parasitise upon phagocytic immune effector cells, a phenomenon that correlates strongly with virulence in rodent models of infection. Despite the importance of phagocyte/Cryptococcus interactions to disease progression, current methods for assaying virulence in the acrophage system are both time consuming and low throughput. Here, we introduce the first stable and fully characterised GFP–expressing derivatives of two widely used cryptococcal strains: C. neoformans serotype A type strain H99 and C. gattii serotype B type strain R265. Both strains show unaltered responses to environmental and host stress conditions and no deficiency in virulence in the macrophage model system. In addition, we report the development of a method to effectively and rapidly investigate macrophage parasitism by flow cytometry, a technique that preserves the accuracy of current approaches but offers a four-fold improvement in speed

A dynamic network approach for the study of human phenotypes

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.Comment: 28 pages (double space), 6 figure

Effect of Biodiversity Changes in Disease Risk: Exploring Disease Emergence in a Plant-Virus System

The effect of biodiversity on the ability of parasites to infect their host and cause disease (i.e. disease risk) is a major question in pathology, which is central to understand the emergence of infectious diseases, and to develop strategies for their management. Two hypotheses, which can be considered as extremes of a continuum, relate biodiversity to disease risk: One states that biodiversity is positively correlated with disease risk (Amplification Effect), and the second predicts a negative correlation between biodiversity and disease risk (Dilution Effect). Which of them applies better to different host-parasite systems is still a source of debate, due to limited experimental or empirical data. This is especially the case for viral diseases of plants. To address this subject, we have monitored for three years the prevalence of several viruses, and virus-associated symptoms, in populations of wild pepper (chiltepin) under different levels of human management. For each population, we also measured the habitat species diversity, host plant genetic diversity and host plant density. Results indicate that disease and infection risk increased with the level of human management, which was associated with decreased species diversity and host genetic diversity, and with increased host plant density. Importantly, species diversity of the habitat was the primary predictor of disease risk for wild chiltepin populations. This changed in managed populations where host genetic diversity was the primary predictor. Host density was generally a poorer predictor of disease and infection risk. These results support the dilution effect hypothesis, and underline the relevance of different ecological factors in determining disease/infection risk in host plant populations under different levels of anthropic influence. These results are relevant for managing plant diseases and for establishing conservation policies for endangered plant species

Associations between cardiorespiratory fitness, physical activity and clustered cardiometabolic risk in children and adolescents: the HAPPY study

Clustering of cardiometabolic risk factors can occur during childhood and predisposes individuals to cardiometabolic disease. This study calculated clustered cardiometabolic risk in 100 children and adolescents aged 10-14 years (59 girls) and explored differences according to cardiorespiratory fitness (CRF) levels and time spent at different physical activity (PA) intensities. CRF was determined using a maximal cycle ergometer test, and PA was assessed using accelerometry. A cardiometabolic risk score was computed as the sum of the standardised scores for waist circumference, blood pressure, total cholesterol/high-density lipoprotein ratio, triglycerides and glucose. Differences in clustered cardiometabolic risk between fit and unfit participants, according to previously proposed health-related threshold values, and between tertiles for PA subcomponents were assessed using ANCOVA. Clustered risk was significantly lower (p < 0.001) in the fit group (mean 1.21 ± 3.42) compared to the unfit group (mean -0.74 ± 2.22), while no differences existed between tertiles for any subcomponent of PA. Conclusion These findings suggest that CRF may have an important cardioprotective role in children and adolescents and highlights the importance of promoting CRF in youth

Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study

BACKGROUND: Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules' performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. METHODS: Electronic databases were searched for systematic reviews of clinical prediction rule studies published between 2006 and 2010. Data were extracted from the eligible validation studies included in the systematic reviews. A meta-analytic meta-epidemiological approach was used to assess the influence of design characteristics on predictive performance. From each validation study, it was assessed whether 7 design and 7 reporting characteristics were properly described. RESULTS: A total of 287 validation studies of clinical prediction rule were collected from 15 systematic reviews (31 meta-analyses). Validation studies using case-control design produced a summary diagnostic odds ratio (DOR) 2.2 times (95% CI: 1.2-4.3) larger than validation studies using cohort design and unclear design. When differential verification was used, the summary DOR was overestimated by twofold (95% CI: 1.2 -3.1) compared to complete, partial and unclear verification. The summary RDOR of validation studies with inadequate sample size was 1.9 (95% CI: 1.2 -3.1) compared to studies with adequate sample size. Study site, reliability, and clinical prediction rule was adequately described in 10.1%, 9.4%, and 7.0% of validation studies respectively. CONCLUSION: Validation studies with design shortcomings may overestimate the performance of clinical prediction rules. The quality of reporting among studies validating clinical prediction rules needs to be improved