Bogged Down Trying to Define Federal Wetlands

This article examines these cases as well as the history, policies, and rationales behind identifying and conserving wetlands. It proposes a unique analytical method for valuation of wetlands. Under the proposed analysis, government agencies and landowners would be required to prepare economic impact statements containing cost/benefit analyses measuring the effects of wetlands delineations upon land values. These analyses would provide the basis for determining the value of preserving wetlands ecosystems as well as the basis for determining fair compensation payable to landowners in the event they suffer land use or income loss as a result of wetlands delineations

The celebrity entrepreneur on television: profile, politics and power

This article examines the rise of the ‘celebrity entrepreneur’ on television through the emergence of the ‘business entertainment format’ and considers the ways in which regular television exposure can be converted into political influence. Within television studies there has been a preoccupation in recent years with how lifestyle and reality formats work to transform ‘ordinary’ people into celebrities. As a result, the contribution of vocationally skilled business professionals to factual entertainment programming has gone almost unnoticed. This article draws on interviews with key media industry professionals and begins by looking at the construction of entrepreneurs as different types of television personalities and how discourses of work, skill and knowledge function in business shows. It then outlines how entrepreneurs can utilize their newly acquired televisual skills to cultivate a wider media profile and secure various forms of political access and influence. Integral to this is the centrality of public relations and media management agencies in shaping media discourses and developing the individual as a ‘brand identity’ that can be used to endorse a range of products or ideas. This has led to policy makers and politicians attempting to mobilize the media profile of celebrity entrepreneurs to reach out and connect with the public on business and enterprise-related issues

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Leadership in the British civil service: an interpretation

This article is essentially a polemic. The argument is that when politicians and officials now talk of ‘leadership’ in the British civil service they do not use that word in the way in which it was previously used. In the past leading civil servants, acting in partnership with ministers and within constitutional constraints, exercised leadership in the sense of setting example, inspiring confidence and encouraging loyalty. The loosening of traditional constitutional patterns, the marginalization of senior officials in the policy process and the emergence of business methods as the preferred model for public ­administration have led to a political and administrative environment in which leadership in the British civil service is now about encouraging patterns of behaviour which fit in with these changes. Leadership skills are now about ‘delivery’; they are not about motivation. It is time for politicians, officials and scholars to be open about this

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics

Heterocyclic scaffolds as promising anticancer agents against tumours of the central nervous system: Exploring the scope of indole and carbazole derivatives

Tumours of the central nervous system are intrinsically more dangerous than tumours at other sites, and in particular, brain tumours are responsible for 3% of cancer deaths in the UK. Despite this, research into new therapies only receives 1% of national cancer research spend. The most common chemotherapies are temozolomide, procarbazine, carmustine, lomustine and vincristine, but because of the rapid development of chemoresistance, these drugs alone simply aren’t sufficient for long-term treatment. Such poor prognosis of brain tumour patients prompted us to research new treatments for malignant glioma, and in doing so, it became apparent that aromatic heterocycles play an important part, especially the indole, carbazole and indolocarbazole scaffolds. This review highlights compounds in development for the treatment of tumours of the central nervous system which are structurally based on the indole, carbazole and indolocarbazole scaffolds, under the expectation that it will highlight new avenues for research for the development of new compounds to treat these devastating neoplasms