Keith Stirling : An introduction to his life and examination of his music

This study introduces the life and examines the music of Australian jazz trumpeter Keith Stirling (1938-2003). The paper discusses the importance and position of Stirling in the jazz culture of Australian music, introducing key concepts that were influential not only to the development of Australian jazz but also in his life. Subsequently, a discussion of Stirling’s metaphoric tendencies provides an understanding of his philosophical perspectives toward improvisation as an art form. Thereafter, a discourse of the research methodology that was used and the resources that were collected throughout the study introduce a control group of transcriptions. These transcriptions provide an origin of phrases with which to discuss aspects of Stirling’s improvisational style. Instrumental approaches and harmonic concepts are then discussed and exemplified through the analysis of the transcribed phrases. Stirling’s instrumental techniques and harmonic concepts are examined by means of his own and student’s hand written notes and quotes from lesson recordings that took place in the early 1980s

Perceived barriers and facilitators for model-informed dosing in pregnancy:a qualitative study across healthcare practitioners and pregnant women

Background: Most women use medication during pregnancy. Pregnancy-induced changes in physiology may require antenatal dose alterations. Yet, evidence-based doses in pregnancy are missing. Given historically limited data, pharmacokinetic models may inform pregnancy-adjusted doses. However, implementing model-informed doses in clinical practice requires support from relevant stakeholders. Purpose:To explore the perceived barriers and facilitators for model-informed antenatal doses among healthcare practitioners (HCPs) and pregnant women. Methods: Online focus groups and interviews were held among healthcare practitioners (HCPs) and pregnant women from eight countries across Europe, Africa and Asia. Purposive sampling was used to identify pregnant women plus HCPs across various specialties prescribing or providing advice on medication to pregnant women. Perceived barriers and facilitators for implementing model-informed doses in pregnancy were identified and categorised using a hybrid thematic analysis. Results: Fifty HCPs and 11 pregnant women participated in 12 focus groups and 16 interviews between January 2022 and March 2023. HCPs worked in the Netherlands (n = 32), the UK (n = 7), South Africa (n = 5), Uganda (n = 4), Kenya, Cameroon, India and Vietnam (n = 1 each). All pregnant women resided in the Netherlands. Barriers and facilitators identified by HCPs spanned 14 categories across four domains whereas pregnant women described barriers and facilitators spanning nine categories within the same domains. Most participants found current antenatal dosing information inadequate and regarded model-informed doses in pregnancy as a valuable and for some, much-needed addition to antenatal care. Although willingness-to-follow model-informed antenatal doses was high across both groups, several barriers for implementation were identified. HCPs underlined the need for transparent model validation and endorsement of the methodology by recognised institutions. Foetal safety was deemed a critical knowledge gap by both groups. HCPs’ information needs and preferred features for model-informed doses in pregnancy varied. Several pregnant women expressed a desire to access information and partake in decisions on antenatal dosing. Conclusions: Given the perceived limitations of current pharmacotherapy for pregnant women and foetuses, model-informed dosing in pregnancy was seen as a promising means to enhance antenatal care by pregnant women and healthcare practitioners.</p

Realistic Adversarial Data Augmentation for MR Image Segmentation

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios

Learning to Segment Microscopy Images with Lazy Labels

The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, we introduce a deep convolutional neural network for microscopy image segmentation. Annotation issues are circumvented by letting the network being trainable on coarse labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy `lazy' labels. Image segmentation is stratified into three connected tasks: rough inner region detection, object separation and pixel-wise segmentation. These tasks are learned in an end-to-end multi-task learning framework. The method is demonstrated on two microscopy datasets, where we show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of annotated data. It brings more flexibility and efficiency for training deep neural networks that are data hungry and is applicable to biomedical images with poor contrast at the object boundaries or with diverse textures and repeated patterns

Evaluation of CNN-Based Human Pose Estimation for Body Segment Lengths Assessment

Human pose estimation (HPE) methods based on convolutional neural networks (CNN) have demonstrated significant progress and achieved state-of-the-art results on human pose datasets. In this study, we aimed to assess the perfor-mance of CNN-based HPE methods for measuring anthropometric data. A Vicon motion analysis system as the reference system and a stereo vision system recorded ten asymptomatic subjects standing in front of the stereo vision system in a static posture. Eight HPE methods estimated the 2D poses which were transformed to the 3D poses by using the stereo vision system. Percentage of correct keypoints, 3D error, and absolute error of the body segment lengths are the evaluation measures which were used to assess the results. Percentage of correct keypoints – the stand-ard metric for 2D pose estimation – showed that the HPE methods could estimate the 2D body joints with a minimum accuracy of 99%. Meanwhile, the average 3D error and absolute error for the body segment lengths are 5 cm

The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma

Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France. Topics brought to consensus included the following: (1) approaches to reporting of Gleason patterns 4 and 5 quantities, and minor/tertiary patterns, (2) an agreement to report the presence of invasive cribriform carcinoma, (3) an agreement to incorporate intraductal carcinoma into grading, and (4) individual versus aggregate grading of systematic and multiparametric magnetic resonance imaging-targeted biopsies. Finally, developments in the field of artificial intelligence in the grading of prostatic carcinoma and future research perspectives were discussed

Protective Immunity Induced with the RTS,S/AS Vaccine Is Associated with IL-2 and TNF-α Producing Effector and Central Memory CD4+ T Cells

A phase 2a RTS,S/AS malaria vaccine trial, conducted previously at the Walter Reed Army Institute of Research, conferred sterile immunity against a primary challenge with infectious sporozoites in 40% of the 80 subjects enrolled in the study. The frequency of Plasmodium falciparum circumsporozoite protein (CSP)-specific CD4+ T cells was significantly higher in protected subjects as compared to non-protected subjects. Intrigued by these unique vaccine-related correlates of protection, in the present study we asked whether RTS,S also induced effector/effector memory (TE/EM) and/or central memory (TCM) CD4+ T cells and whether one or both of these sub-populations is the primary source of cytokine production. We showed for the first time that PBMC from malaria-non-exposed RTS,S-immunized subjects contain both TE/EM and TCM cells that generate strong IL-2 responses following re-stimulation in vitro with CSP peptides. Moreover, both the frequencies and the total numbers of IL-2-producing CD4+ TE/EM cells and of CD4+ TCM cells from protected subjects were significantly higher than those from non-protected subjects. We also demonstrated for the first time that there is a strong association between the frequency of CSP peptide-reactive CD4+ T cells producing IL-2 and the titers of CSP-specific antibodies in the same individual, suggesting that IL-2 may be acting as a growth factor for follicular Th cells and/or B cells. The frequencies of CSP peptide-reactive, TNF-α-producing CD4+ TE/EM cells and of CD4+ TE/EM cells secreting both IL-2 and TNF-α were also shown to be higher in protected vs. non-protected individuals. We have, therefore, demonstrated that in addition to TNF-α, IL-2 is also a significant contributing factor to RTS,S/AS vaccine induced immunity and that both TE/EM and TCM cells are major producers of IL-2

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided