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Tumor cells exhibit unique metabolic adaptations that are increasingly viewed as potential targets for novel and specific cancer therapies. Among these targets, the carnitine palmitoyltransferase system is responsible for delivering the long-chain fatty acid (FA) from cytoplasm into mitochondria for oxidation, where carnitine palmitoyltransferase I (CPTI) catalyzes the rate-limiting step of fatty acid oxidation (FAO). With increasing understanding of the crucial role had by fatty acid oxidation in cancer, CPTI has received renewed attention as a pivotal mediator in cancer metabolic mechanism. CPTI activates FAO and fuels cancer growth via ATP and NADPH production, constituting an essential part of cancer metabolism adaptation. Moreover, CPTI also functionally intertwines with other key pathways and factors to regulate gene expression and apoptosis of cancer cell. Here, we summarize recent findings and update the current understanding of FAO and CPTI in cancer and provide theoretical basis for this enzyme as an emerging potential molecular target in cancer therapeutic intervention.

Dapsone is an important medication for the treatment of leprosy, but a life-threatening drug hypersensitivity syndrome develops in some patients. In this report from China, an HLA-B locus is identified as a strong genetic risk factor for the syndrome. Dapsone (4-4′-sulfonyldianiline), which was first synthesized in 1908, 1 is both an antibiotic and an antiinflammatory agent. Dapsone alone or in combination with other drugs has been used for the prevention and treatment of infectious diseases (e.g., leprosy, malaria, and actinomycetoma, as well as Pneumocystis jirovecii pneumonia in persons with human immunodeficiency virus [HIV] infection) and chronic inflammatory diseases characterized by the infiltration of neutrophils or eosinophils (e.g., dermatitis herpetiformis, linear IgA dermatosis, subcorneal pustular dermatosis, and erythema elevatum diutinum). 2 , 3 About 0.5 to 3.6% of persons treated with dapsone have a drug hypersensitivity syndrome, 3 – 5 which was first described by . . .

Lagged oceanic and atmospheric climate indices are potentially useful predictors of seasonal rainfall totals. A rigorous Bayesian joint probability modeling approach is applied to find the cross-validation predictive densities of gridded Australian seasonal rainfall totals using lagged climate indices as predictors over the period of 1950–2009. The evidence supporting the use of each climate index as a predictor of seasonal rainfall is quantified by the pseudo-Bayes factor based on cross-validation predictive densities. The evidence strongly supports the use of climate indices from the Pacific region with weaker, but positive, evidence for the use of climate indices from the Indian region and the extratropical region. The spatial structure and seasonal variation of the evidence for each climate index is mapped and compared. Spatially, the strongest supporting evidence is found for forecasting in northern and eastern Australia. Seasonally, the strongest evidence is found from August–October to November–January and the weakest evidence is found from March–May to May–July. In some regions and seasons, there is little evidence supporting the use of climate indices for forecasting seasonal rainfall. Climate indices derived from sea surface temperature anomalies in the Pacific region show stronger persistence in the relationship with Australian seasonal rainfall totals than climate indices derived from sea surface temperature anomalies in the Indian region. Climate indices derived from atmospheric variables are also strongly supported, provided they represent the large-scale circulation. Many climate indices are found to show similar supporting evidence for forecasting Australian seasonal rainfall, leading to the prospect of combining climate indices in multiple predictor models and/or model averaging.

Background: Over the past two decades, China has enjoyed impressive economic development, and her citizens have experienced many remarked changes in their lifestyle. These changes are often associated with an increase in obesity and chronic disease. Methods: In this meta-analysis, based on nationally representative data, we studied the current prevalence of obesity and the trends in obesity, mortality and morbidity in China. Results: Between 1992 and 2002, the prevalence of overweight and obesity increased in all gender and age groups and in all geographic areas. Using the World Health Organization body mass index cut points, the combined prevalence of overweight and obesity increased from 14.6 to 21.8%. The Chinese obesity standard shows an increase from 20.0 to 29.9%. The annual increase rate was highest in men aged 18-44 years and women aged 45-59 years (approximately 1.6 and 1.0% points, respectively). In general, male subjects, urban residents, and high-income groups had a greater increase. With the increase in overweight and obesity, obesity-, and diet-related chronic diseases (e.g., hypertension, cardiovascular disease (CVD), and type 2 diabetes) also increased over the past decade and became a more important preventable cause of death. Hypertension increased from 14.4% in 1991 to 18.8% in 2002 in adults; in older adults aged 35-74 years, it increased from 19.7 to 28.6%. Between 1993 and 2003, the prevalence of CVD increased from 31.4 to 50.0%; diabetes increased from 1.9 to 5.6%. During 1990-2003, although total mortality rate (per 100 000) decreased, overall the mortality rate and contribution (as percentages) to total death of obesity-related chronic disease increased, in particular, in rural areas. Mortality rate (per 100 000) of CVD increased from 128 to 145 and its contribution to total death, 27 to 32%, in rural areas; the figures decreased slightly in urban areas. The mortality rate of 'nutrition, endocrinology and metabolism-related disease' (NEMD) increased in both rural and urban areas between 1990 and 2000, 8.0 to 10.6 and 4.9 to 5.3, respectively. The current prevalence of hypertension, dyslipidaemia, metabolic syndrome, and diabetes among Chinese adults is approximately 20, 20, 15, and 3%, respectively. Conclusion: The prevalence of overweight and obesity and obesity-related chronic diseases have increased in China in the past decade. Our findings provide useful information for the projection of future trends and the formulation of national strategies and programmes that can address the challenges of the growing obesity and chronic disease epidemic.

GCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called “coherence.” This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw forecasts are not significantly positively correlated with observations, QM tends to produce negatively skillful forecasts. Even when there is significant positive correlation, QM cannot ensure reliability and coherence for postprocessed forecasts. Therefore, QM is not a fully satisfactory method for postprocessing forecasts where the issues of bias, reliability, and coherence pre-exist. Alternative postprocessing methods based on ensemble model output statistics (EMOS) are available that achieve not only unbiased but also reliable and coherent forecasts. This is shown with one such alternative, the Bayesian joint probability modeling approach.

Merging forecasts from multiple models has the potential to combine the strengths of individual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging (BMA) method for merging forecasts from multiple models, giving greater weights to better performing models. The study aims for a BMA method that is capable of producing relatively stable weights in the presence of significant sampling variability, leading to robust forecasts for future events. The BMA method is applied to merge forecasts from multiple statistical models for seasonal rainfall forecasts over Australia using climate indices as predictors. It is shown that the fully merged forecasts effectively combine the best skills of the models to maximize the spatial coverage of positive skill. Overall, the skill is low for the first half of the year but more positive for the second half of the year. Models in the Pacific group contribute the most skill, and models in the Indian and extratropical groups also produce useful and sometimes distinct skills. The fully merged probabilistic forecasts are found to be reliable in representing forecast uncertainty spread. The forecast skill holds well when forecast lead time is increased from 0 to 1 month. The BMA method outperforms the approach of using a model with two fixed predictors chosen a priori and the approach of selecting the best model based on predictive performance.

Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO–temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.

Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

Metastasis is a multistep process starting with the dissemination of tumor cells from a primary site and ending with secondary tumor development in an anatomically distant location. The epithelial–mesenchymal transition (EMT), a process that endows epithelial tumor cells with mesenchymal properties including reduced adhesion and increased motility, is considered a critical step driving the early phase of cancer metastasis. Although significant progress has been made in understanding the molecular characteristics of EMT, the intracellular mechanisms driving transition through the various stages of EMT remain unclear. In recent years, an increasing number of studies have demonstrated the involvement of long non-coding RNAs (lncRNAs) in tumor metastasis through modulating EMT. LncRNAs and their associated signaling networks have now emerged as new players in the induction and regulation of EMT during metastasis. Here we summarize the recent findings and characterizations of several known lncRNAs involved in the regulation of EMT. We will also discuss the potential use of these lncRNAs as diagnostic and prognostic biomarkers as well as therapeutic targets to slow down or prevent metastatic spread of malignant tumors.