The experience of lay singers in Anglican cathedrals, and its spiritual reality: theological, institutional and historical influences in understanding the Anglican Choral Tradition

The choral foundations of the Church of England sustain a well-established tradition of choral music. This has been subject to musicological study (repertory/performance practice), sociological investigation (education/gender) and reflection by the Church (congregational growth). Despite the centrality of their role, the experiences of adult lay singers have only received superficial treatment. This study addresses this gap through qualitative study. With no previous studies to draw on, the initial research question focused on identifying the spiritual experiences of lay singers. Data was gathered in two stages: group conversations with 30 participants identifying significant themes, followed by seven detailed one-to-one interviews providing the central data for analysis. Both stages of data gathering included a cross-section of institutional settings (historic cathedrals of both ‘old’ and ‘new’ foundation, ‘parish church cathedrals’ and academic choral foundations). This data is analysed utilising three lenses – systematic theology, ecclesiology, and ecclesiastical history. Articulating disparate personal patterns of experience and understanding, the study identifies how those patterns express a strong corporate account of meaning attributed to musical performance in the liturgy. This corporate narrative correlates closely to definably Anglican patterns of understanding in all three areas of analysis; demonstrating the way experiences can best be understood through a triangulation of theological, institutional and historical factors. Located at the intersection of Music Theology and Practical Theology, this study identifies diverse individual accounts from participants that are unified by a strong corporate sense of meaning; highlighting the importance of corporate meaning-making. Musicologically, it identifies spiritual meaning within the performance setting of Anglican choral foundations. These corporate understandings of liturgical activity are also important for a Church seeking to identify key features of a context that provides an atypical example of congregational growth

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes

Rectal artesunate suppositories for the pre-referral treatment of suspected severe malaria

In this Policy Forum article, James A. Watson and colleagues discuss recent guidelines relating to pre-referral treatment of suspected severe malaria with rectal artesunate suppositories in remote areas

Reconstitution Properties of Thymus Stem Cells in Murine Fetal Liver

Injection of day-12 murine fetal liver cells into thymus lobes of Thy-1 congenic adult recipients results in a wave of thymocyte development. The kinetics of repopulation by donor cells reaches a peak after 20–25 days. The frequency of thymic stem cells (TSC) in day-12 fetal liver was estimated, by limit dilution, as 1 in 4x104 cells. Within 8 hr of injection into a thymus lobe, fetal liver TSC commit to T-cell development, losing stem-cell activity. When fetal liver cells are maintained in culture for 7 days, with no exogenous cytokines added, and then injected intra-thymically (I.T.), thymus recolonization is not observed. However, TSC can be maintained in culture for 7 days with IL-1β, IL-3, IL-6, or LIF added, alone or in combination, with steel factor (SLF). Poisson analysis of fetal liver cells cultured with SLF and IL-3 together revealed a precursor frequency of 1 in 1.8x 105 cells. In contrast, the frequency of TSC in adult bone marrow was estimated by limit dilution as 1 in 12,000 cells

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments

WHO should accelerate, not stall, rectal artesunate deployment for pre-referral treatment of severe malaria

The recent World Health Organization moratorium on rectal artesunate (RAS) for pre-referral treatment of severe childhood malaria is costing young lives. The decision was based on disappointing findings from a large observational study that provided RAS to community health workers with little training and supervision. This non-randomized, operational research has provided useful information to guide the implementation of RAS but is subject to bias and confounding and cannot be used to assess treatment effects. Parenteral artesunate reduces severe malaria mortality and a large body of evidence also shows RAS has lifesaving efficacy. There is now more than a decade of delay in conducting the necessary engagement and training required for successful deployment of RAS. Further delays will result in more preventable deaths

A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage

The quality of potato tubers is dependent on several attributes been maintained at appropriate levels during storage. One of these attributes is sprouting activity that is initiated from meristematic regions of the tubers (eyes). Sprouting activity is a major problem that contributes to reduced shelf life and elevated sugar content, which affects the marketability of seed tubers as well as fried products. This study compared the capabilities of three different optical systems (1: visible/near-infrared (Vis/NIR) interactance spectroscopy, 2: Vis/NIR hyperspectral imaging, 3: NIR transmittance) and machine learning methods to detect sprouting activity in potatoes based on the primordial leaf count (LC). The study was conducted on Frito Lay 1879 and Russet Norkotah cultivars stored at different temperatures and classification models were developed that considered both cultivars combined and classified the tubers as having either high or low sprouting activity. Measurements were performed on whole tubers and sliced samples to see the effect this would have on identifying sprouting activity. Sequential forward selection was applied for wavelength selection and the classification was carried out using K-nearest neighbor, partial least squares discriminant analysis, and soft independent modeling class analogy. The highest classification accuracy values obtained by the hyperspectral imaging system and was 87.5% and 90% for sliced and whole samples, respectively. Data fusion did not show classification improvement for whole tubers, whereas a 7.5% classification accuracy increase was illustrated for sliced samples. By investigating different optical techniques and machine learning methods, this study provides a first step toward developing a handheld optical device for early detection of sprouting activity, enabling advanced aid potato storage management

Using citizen science to identify Australia’s least known birds and inform conservation action

Citizen science is a popular approach to biodiversity surveying, whereby data that are collected by volunteer naturalists may help analysts to understand the distribution and abundance of wild organisms. In Australia, birdwatchers have contributed to two major citizen science programs, eBird (run by the Cornell Lab of Ornithology) and Birdata (run by Birdlife Australia), which collectively hold more than 42 million records of wild birds from across the country. However, these records are not evenly distributed across space, time, or taxonomy, with particularly significant variation in the number of records of each species in these datasets. In this paper, we explore this variation and seek to determine which Australian bird species are least known as determined by rates of citizen science survey detections. We achieve this by comparing the rates of survey effort and species detection across each Australian bird species? range, assigning all 581 species to one of the four groups depending on their rates of survey effort and species observation. We classify 56 species into a group considered the most poorly recorded despite extensive survey effort, with Coxen?s Fig Parrot Cyclopsitta coxeni, Letter-winged Kite Elanus scriptus, Night Parrot Pezoporus occidentalis, Buff-breasted Buttonquail Turnix olivii and Red-chested Buttonquail Turnix pyrrhothorax having the very lowest numbers of records. Our analyses provide a framework to identify species that are poorly represented in citizen science datasets. We explore the reasons behind why they may be poorly represented and suggest ways in which targeted approaches may be able to help fill in the gaps.Publisher PDFPeer reviewe

Primaquine in glucose-6-phosphate dehydrogenase deficiency: an adaptive pharmacometric assessment of ascending dose regimens in healthy volunteers

Background: Primaquine is an 8-aminoquinoline antimalarial. It is the only widely available treatment to prevent relapses of Plasmodium vivax malaria. The 8-aminoquinolines cause dose-dependent haemolysis in glucose-6-phosphate dehydrogenase deficiency (G6PDd). G6PDd is common in malaria endemic areas but testing is often not available. As a consequence primaquine is underused. Methods: We conducted an adaptive pharmacometric study to characterise the relationship between primaquine dose and haemolysis in G6PDd. The aim was to explore shorter and safer primaquine radical cure regimens compared to the currently recommended 8-weekly regimen (0.75 mg/kg once weekly), potentially obviating the need for G6PD testing. Hemizygous G6PDd healthy adult Thai and Burmese male volunteers were admitted to the Hospital for Tropical Diseases in Bangkok. In Part 1, volunteers were given ascending dose primaquine regimens whereby daily doses were increased from 7.5 mg up to 45 mg over 15–20 days. In Part 2 conducted at least 6 months later, a single primaquine 45 mg dose was given. Results: 24 volunteers were enrolled in Part 1, and 16 in Part 2 (13 participated in both studies). In three volunteers, the ascending dose regimen was stopped because of haemolysis (n=1) and asymptomatic increases in transaminases (n=2; one was hepatitis E positive). Otherwise the ascending regimens were well tolerated with no drug-related serious adverse events. In Part 1, the median haemoglobin concentration decline was 3.7 g/dL (range: 2.1–5.9; relative decline of 26% [range: 15–40%]). Primaquine doses up to 0.87 mg/kg/day were tolerated subsequently without clinically significant further falls in haemoglobin. In Part 2, the median haemoglobin concentration decline was 1.7 g/dL (range 0.9–4.1; relative fall of 12% [range: 7–30% decrease]). The ascending dose primaquine regimens gave seven times more drug but resulted in only double the haemoglobin decline. Conclusions: In patients with Southeast Asian G6PDd variants, full radical cure treatment can be given in under 3 weeks compared with the current 8-week regimen. Funding: Medical Research Council of the United Kingdom (MR/R015252/1) and Wellcome (093956/Z/10/C, 223253/Z/21/Z)