Student Research Projects With Industrial Impact

Abstract This paper describes six final year undergraduate research projects supported by a collaboration between the Whittle Laboratory at the University of Cambridge and Reaction Engines (RE), a UK aerospace company. The collaboration is now in its fourth year of projects relating to RE's synergetic air breathing rocket engine (SABRE). The approach taken in these projects combines modern teaching pedagogy with a best practice methodology for industrial-academic collaboration and a well established framework for structuring research problems. This paper explains how the three methodologies are tailored and adapted for use with final year undergraduate research projects. The approach is mapped on to an annual project cycle which begins with the industry and academic partners deciding which topics to investigate and proceeds through student selection, the project work itself and concludes with student assessment and end-of-year reporting. The projects combine analytical, computational and experimental work and have covered counter-rotating turbomachinery, S-ducts in compressors and Helium Turbine design, all of which are topics of primary importance to the design of SABRE. Following descriptions of each of the six completed projects, the impact of the work and lessons learned are considered from the point of view of the students, the industrial partner and the academic supervisors. Overall, the students found the work extremely engaging and have all been encouraged to pursue careers in engineering, either in industry or through postgraduate study. For the industry partner the collaboration provides expertise and an approach which is not available in-house as well providing a “second look” at key technical questions. For the academics involved, the opportunity to lead research on a “real” problem with an industrial partner has proved highly motivating as well as providing opportunities for personal and career development.The student projects were funded by Reaction Engine

Linguistic and semantic factors in government e-petitions: A comparison between the United Kingdom and the United States of America

Many legislators around the word are offering the use of web based e-petitioning platforms to allow their electorate to influence government policy and action. A popular e-petition can gain much coverage, both in traditional media and social media. The task then becomes how to understand what features may make an e-petition popular and hence, potentially influential. One area of investigation is the linguistic and topical content of the supporting e-petition text. This study takes an existing methodology previously applied to the American government's e-petition platform and replicates the study for the United Kingdom's equivalent platform. This allows an insight into not only the United Kingdom's e-petition process but also a comparison with a similar platform. We find that when assessing an e-petition's popularity, the control variables are significant in both countries, e-petitions in the United Kingdom are more popular if some named entities are used in the text, and that topics are commonly more influential in America

A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques

Introduction: Mass appraisals in the rental housing market are far less common than those in the sales market. However, there is evidence for substantial growth in the rental market and this lack of insight hampers commercial organisations and local and national governments in understanding this market. Case description: This case study uses data that are supplied from a property listings web site and are unique in their scale, with over 1.2 million rental property listings available over a two year period. The data is analysed in a large data institute using generalised linear regression, machine learning and a pseudo practitioner based approach. Discussion and Evaluation: The study should be seen as a practical guide for property professionals and academics wishing to undertake such appraisals and looking for guidance on the best methods to use. It also provides insight into the property characteristics which most influence rental listing price. Conclusions: From the regression analysis, attributes that increase the rental listing price are: the number of rooms in the property, proximity to central London and to railway stations, being located in more affluent neighbourhoods and being close to local amenities and better performing schools. Of the machine learning algorithms used, the two tree based approaches were seen to outperform the regression based approaches. In terms of a simple measure of the median appraisal error, a practitioner based approach is seen to outperform the modelling approaches. A practical finding is that the application of sophisticated machine learning algorithms to big data is still a challenge for modern desktop PCs

Endoscopic navigation in the absence of CT imaging

Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference image to provide structural context to the clinician. In this paper, we present a system for navigation during clinical endoscopic exploration in the absence of computed tomography (CT) scans by making use of shape statistics from past CT scans. Using a deformable registration algorithm along with dense reconstructions from video, we show that we are able to achieve submillimeter registrations in in-vivo clinical data and are able to assign confidence to these registrations using confidence criteria established using simulated data.Comment: 8 pages, 3 figures, MICCAI 201

Parity Doubling and the S Parameter Below the Conformal Window

We describe a lattice simulation of the masses and decay constants of the lowest-lying vector and axial resonances, and the electroweak S parameter, in an SU(3) gauge theory with $N_f = 2$ and 6 fermions in the fundamental representation. The spectrum becomes more parity doubled and the S parameter per electroweak doublet decreases when $N_f$ is increased from 2 to 6, motivating study of these trends as $N_f$ is increased further, toward the critical value for transition from confinement to infrared conformality.Comment: 4 pages, 5 figures; to be submitted to PR

The effect of dynamical scattering on single-plane phase retrieval in electron ptychography

Segmented and pixelated detectors on scanning transmission electron microscopes enable the complex specimen transmission function to be reconstructed. Imaging the transmission function is key to interpreting the electric and magnetic properties of the specimen, and as such four-dimensional scanning transmission electron microscopy (4D-STEM) imaging techniques are crucial for our understanding of functional materials. Many of the algorithms used in the reconstruction of the transmission function rely on the multiplicative approximation and the (weak) phase object approximation, which are not valid for many materials, particularly at high resolution. Herein, we study the breakdown of simple phase imaging in thicker samples. We demonstrate the behavior of integrated center of mass imaging, single-side band ptychography, and Wigner distribution deconvolution over a thickness series of simulated GaN 4D-STEM datasets. We further give guidance as to the optimal focal conditions for obtaining a more interpretable dataset using these algorithms

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.

Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories

Regulation of immune responses in primary biliary cholangitis: a transcriptomic analysis of peripheral immune cells

BACKGROUND AIMS: In patients with primary biliary cholangitis (PBC), the serum liver biochemistry measured during treatment with ursodeoxycholic acid-the UDCA response-accurately predicts long-term outcome. Molecular characterization of patients stratified by UDCA response can improve biological understanding of the high-risk disease, thereby helping to identify alternative approaches to disease-modifying therapy. In this study, we sought to characterize the immunobiology of the UDCA response using transcriptional profiling of peripheral blood mononuclear cell subsets. METHODS: We performed bulk RNA-sequencing of monocytes and TH1, TH17, TREG, and B cells isolated from the peripheral blood of 15 PBC patients with adequate UDCA response ("responders"), 16 PBC patients with inadequate UDCA response ("nonresponders"), and 15 matched controls. We used the Weighted Gene Co-expression Network Analysis to identify networks of co-expressed genes ("modules") associated with response status and the most highly connected genes ("hub genes") within them. Finally, we performed a Multi-Omics Factor Analysis of the Weighted Gene Co-expression Network Analysis modules to identify the principal axes of biological variation ("latent factors") across all peripheral blood mononuclear cell subsets. RESULTS: Using the Weighted Gene Co-expression Network Analysis, we identified modules associated with response and/or disease status (q<0.05) in each peripheral blood mononuclear cell subset. Hub genes and functional annotations suggested that monocytes are proinflammatory in nonresponders, but antiinflammatory in responders; TH1 and TH17 cells are activated in all PBC cases but better regulated in responders; and TREG cells are activated-but also kept in check-in responders. Using the Multi-Omics Factor Analysis, we found that antiinflammatory activity in monocytes, regulation of TH1 cells, and activation of TREG cells are interrelated and more prominent in responders. CONCLUSIONS: We provide evidence that adaptive immune responses are better regulated in patients with PBC with adequate UDCA response

Disease severity adversely affects delivery of dialysis in acute renal failure

Background/Aims: Methods of intermittent hemodialysis (IHD) dose quantification in acute renal failure (ARF) are not well defined. This observational study was designed to evaluate the impact of disease activity on delivered single pool Kt/V-urea in ARF patients. Methods: 100 patients with severe ARF (acute intrinsic renal disease in 18 patients, nephrotoxic acute tubular necrosis in 38 patients, and septic ARF in 44 patients) were analyzed during four consecutive sessions of IHD, performed for 3.5-5 h every other day or daily. Target IHD dose was a single pool Kt/V-urea of 1.2 or more per dialysis session for all patients. Prescribed Kt/V-urea was calculated from desired dialyzer clearance (K), desired treatment time (t) and anthropometric estimates for urea distribution volume (V). The desired clearance (K) was estimated from prescribed blood flow rate and manufacturer's charts of in vivo data obtained in maintenance dialysis patients. Delivered single pool Kt/V-urea was calculated using the Daugirdas equation. Results: None of the patients had prescription failure of the target dose. The delivered IHD doses were substantially lower than the prescribed Kt/V values, particularly in ARF patients with sepsis/septic shock. Stratification according to disease severity revealed that all patients with isolated ARF, but none with 3 or more organ failures and none who needed vasopressive support received the target dose. Conclusion: Prescription of target IHD dose by single pool Kt/V-urea resulted in suboptimal dialysis dose delivery in critically ill patients. Numerous patient-related and treatment-immanent factors acting in concert reduced the delivered dose. Copyright (C) 2007 S. Karger AG, Basel