The role of dietary polyphenols in the moderation of the inflammatory response in early stage colorectal cancer

Current focus in colorectal cancer management is on reducing overall mortality by increasing the number of early stage cancers diagnosed and treated with curative intent. Despite the success of screening programmes in down-staging colorectal cancer, interval cancer rates are substantial and other strategies are desirable. Sporadic colorectal cancer is largely associated with lifestyle factors including diet. Polyphenols are phytochemicals ingested as part of a normal diet which are abundant in plant foods including fruits/berries and vegetables. These may exert their anti-carcinogenic effects via the modulation of inflammatory pathways. Key signal transduction pathways are fundamental to the association of inflammation and disease progression including those mediated by NF-κB and STAT, PI3K and COX. Our aim was to examine the evidence for the effect of dietary polyphenols intake on tumour and host inflammatory responses to determine if polyphenols may be effective as part of a dietary intervention. There is good epidemiological evidence of a reduction in colorectal cancer risk from case-control and cohort studies assessing polyphenol intake. It would be premature to suggest a major public health intervention to promote their consumption however, dietary change is safe and feasible, emphasising the need for further investigation of polyphenols and colorectal cancer risk

Can students' feedback literacy be improved? A scoping review of interventions

Student feedback literacy has been the subject of much conceptual literature; however, relatively little intervention research has investigated how and if it can be developed. Further, no evaluation of the current empirical literature has been conducted to assess which elements of feedback literacy can be successfully improved in practice, and which elements need further investigation. This paper seeks to explore how different aspects of feedback literacy have been developed in higher education. A scoping review was conducted to address the foci, nature and success of interventions. The review found evidence that educational interventions enhanced feedback literacy in students, such as managing perceptions and attitudes, and having more confidence and agency in the feedback process. While some interventions have an effect on influencing student feedback literacy, both improved study design and intervention design are required to make the most of future feedback literacy interventions

MissForest - nonparametric missing value imputation for mixed-type data

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a nonparametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple data sets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in data sets including different types of variables. In our comparative study missForest outperforms other methods of imputation especially in data settings where complex interactions and nonlinear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data.Comment: Submitted to Oxford Journal's Bioinformatics on 3rd of May 201

Propfan Test Assessment (PTA)

The objectives of the Propfan Test Assessment (PTA) Program were to validate in flight the structural integrity of large-scale propfan blades and to measure noise characteristics of the propfan in both near and far fields. All program objectives were met or exceeded, on schedule and under budget. A Gulfstream Aerospace Corporation GII aircraft was modified to provide a testbed for the 2.74m (9 ft) diameter Hamilton Standard SR-7 propfan which was driven by a 4475 kw (600 shp) turboshaft engine mounted on the left-hand wing of the aircraft. Flight research tests were performed for 20 combinations of speed and altitude within a flight envelope that extended to Mach numbers of 0.85 and altitudes of 12,192m (40,000 ft). Propfan blade stress, near-field noise on aircraft surfaces, and cabin noise were recorded. Primary variables were propfan power and tip speed, and the nacelle tilt angle. Extensive low altitude far-field noise tests were made to measure flyover and sideline noise and the lateral attenuation of noise. In coopertion with the FAA, tests were also made of flyover noise for the aircraft at 6100m (20,000 ft) and 10,668m (35,000 ft). A final series of tests were flown to evaluate an advanced cabin wall noise treatment that was produced under a separate program by NASA-Langley Research Center

Space Transportation System Meteorological Expert

Computers are being used today to build the expert systems of tomorrow. Expert systems are computer programs that are smart about a domain in the way that people are smart. Expert systems technology is being applied to weather forecasting to support Shuttle operations for launch and for ground processing at Kennedy Space Center (KSC), Florida. The Space Transportation System Meterological ExperT (STSMET) is a long term project, now-in its third year, to capture general Shuttle operational weather forecasting expertise specific to our locale, to apply it to Shuttle operational weather forecasting tasks at the Cape Canaveral Forecast Facility (CCFF) at the Cape Canaveral Air Force Station (CCAFS), and to ultimately provide an on-line, real-time operational aid to the duty forecasters in performing their tasks. The first domain addressed by the project has been summer thunderstorms. The effort to represent this knowledge and a control structure to reason about it has resulted in an approach that we call scenario-based reasoning. Other meteorological domains on our agenda are frontal weather phenomena, visibility including fog, and wind shear. We believe that scenario-based reasoning is also applicable to these other meteorological domains. The specific operational tasks to which to apply the general knowledge about summer thunderstorms are being identified during this phase of the contract. The project is being developed using state-of-the-art hardware and software: a Symbolics Lisp Machine, Zetalisp and Automated Reasoning Tool (ART), an expert system shell. Scenario-based reasoning appears to have applications outside of weather forecasting. The abilities of a scenario-based system to reason qualitatively, to reason over time, and to reason across scale are all applicable to planning in autonomous systems. With further research, we expect to add analogical reasoning to the abilities of scenario-based reasoning