Photoelectrochemical fabrication of spectroscopic diffraction gratings, phase 2

This program was directed toward the production of Echelle diffraction gratings by a light-driven, electrochemical etching technique (photoelectrochemical etching). Etching is carried out in single crystal materials, and the differential rate of etching of the different crystallographic planes used to define the groove profiles. Etching of V-groove profiles was first discovered by us during the first phase of this project, which was initially conceived as a general exploration of photoelectrochemical etching techniques for grating fabrication. This highly controllable V-groove etching process was considered to be of high significance for producing low pitch Echelles, and provided the basis for a more extensive Phase 2 investigation

Identification of Dimethyldioctadecylammonium Ion (m/z 550.6) and Related Species (m/z 522.6, 494.6) as a Source of Contamination in Mass Spectrometry

Chemical contamination can be one of the more common problems encountered when performing trace-level analysis regardless of the analytical technique. Minimizing or eliminating background interferences can be a difficult task, so knowledge of the chemical composition of these contaminants can prove invaluable when it comes to identifying the source. Once the source is identified, proper steps may be taken to reduce or eliminate it. In this study, we report the identity of some commonly seen contaminants (m/z 550.6, 522.6, and 494.6) in electrospray ionization (ESI) mass spectrometry (MS). Through MS, tandem MS, accurate-mass, and high-resolution measurements we have identified these background contaminants as being quaternary ammonium species that contain long-chain hydrocarbon groups, where m/z 550.6 is a dimethyldioctadecylammonium ion (C18, C18) and m/z 522.6 and 494.6 are similar in nature but have shorter alkyl-chain groups. The lipophilic nature of these compounds and the fact that they have molecular weights similar to lysophospholipids make them a frequent contaminant in lipidomic studies. The likely sources of these compounds are commonly used personal and household products

Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed

Creating clinical pharmacy capacity in Namibia: a collaboration to establish a post-graduate pharmacy degree programme

Namibia has previously relied on external training of pharmacists but began in-country training in 2011. In response to an identified need for postgraduate clinical pharmacy development and training in the country, a Master’s degree was set up at the University of Namibia in 2016. The country has a considerable health burden of HIV and TB as well as a shortage of healthcare professionals. A UK clinical diploma model was adapted to meet the specific needs of the country and wider region, ensuring students could access the course over a sparsely populated, but large geographical spread, in addition to providing work-based learning, embedding research skills for future development, and focusing on the health needs of Namibia. The course uses online learning platforms and contact sessions to cover both knowledge and skill acquisition throughout the 3 years of the course. UK and US clinical pharmacists are utilised to provide specialist input, both remotely and within student workplaces, and further support has come from collaborations, including cross-site visits, with the UK-based pharmacy school whose diploma model was adapted. Challenges have included a shortage of clinical mentors, also compounding the students’ difficulty in visualising their future roles, as well as lone practitioners finding it hard to attend all contact sessions. The initial dropout rates of earlier cohorts have since reduced with greater understanding of the programme, and enthusiasm for the course remains high. The aim for the Master’s is to train students to become competent clinical pharmacists, thus having the knowledge and skills to mentor future cohorts of the course, as well as expanding the specialty within the country

Which bills are lobbied? Predicting and interpreting lobbying activity in the US

Using lobbying data from OpenSecrets.org, we offer several experiments applying machine learning techniques to predict if a piece of legislation (US bill) has been subjected to lobbying activities or not. We also investigate the influence of the intensity of the lobbying activity on how discernible a lobbied bill is from one that was not subject to lobbying. We compare the performance of a number of different models (logistic regression, random forest, CNN and LSTM) and text embedding representations (BOW, TF-IDF, GloVe, Law2Vec). We report results of above 0.85\% ROC AUC scores, and 78\% accuracy. Model performance significantly improves (95\% ROC AUC, and 88\% accuracy) when bills with higher lobbying intensity are looked at. We also propose a method that could be used for unlabelled data. Through this we show that there is a considerably large number of previously unlabelled US bills where our predictions suggest that some lobbying activity took place. We believe our method could potentially contribute to the enforcement of the US Lobbying Disclosure Act (LDA) by indicating the bills that were likely to have been affected by lobbying but were not filed as such

Mice with altered serotonin 2C receptor RNA editing display characteristics of Prader–Willi syndrome

RNA transcripts encoding the 2C-subtype of serotonin (5HT2C) receptor undergo up to five adenosine-to-inosine editing events to encode twenty-four protein isoforms. To examine the effects of altered 5HT2C editing in vivo, we generated mutant mice solely expressing the fully-edited (VGV) isoform of the receptor. Mutant animals present phenotypic characteristics of Prader-Willi Syndrome (PWS) including a failure to thrive, decreased somatic growth, neonatal muscular hypotonia, and reduced food consumption followed by post-weaning hyperphagia. Though previous studies have identified alterations in both 5HT2C receptor expression and 5HT2C-mediated behaviors in both PWS patients and mouse models of this disorder, to our knowledge the 5HT2C gene is the first locus outside the PWS imprinted region in which mutations can phenocopy numerous aspects of this syndrome. These results not only strengthen the link between the molecular etiology of PWS and altered 5HT2C expression, but also demonstrate the importance of normal patterns of 5HT2C RNA editing in vivo

Obesity and altered glucose metabolism impact HDL composition in CETP transgenic mice: a role for ovarian hormones

Mechanisms underlying changes in HDL composition caused by obesity are poorly defined, partly because mice lack expression of cholesteryl ester transfer protein (CETP), which shuttles triglyceride and cholesteryl ester between lipoproteins. Because menopause is associated with weight gain, altered glucose metabolism, and changes in HDL, we tested the effect of feeding a high-fat diet (HFD) and ovariectomy (OVX) on glucose metabolism and HDL composition in CETP transgenic mice. After OVX, female CETP-expressing mice had accelerated weight gain with HFD-feeding and impaired glucose tolerance by hyperglycemic clamp techniques, compared with OVX mice fed a low-fat diet (LFD). Sham-operated mice (SHAM) did not show HFD-induced weight gain and had less glucose intolerance than OVX mice. Using shotgun HDL proteomics, HFD-feeding in OVX mice had a large effect on HDL composition, including increased levels of apoA2, apoA4, apoC2, and apoC3, proteins involved in TG metabolism. These changes were associated with decreased hepatic expression of SR-B1, ABCA1, and LDL receptor, proteins involved in modulating the lipid content of HDL. In SHAM mice, there were minimal changes in HDL composition with HFD feeding. These studies suggest that the absence of ovarian hormones negatively influences the response to high-fat feeding in terms of glucose tolerance and HDL composition. CETP-expressing mice may represent a useful model to define how metabolic changes affect HDL composition and function