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

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./

A low-cost HPV immunochromatographic assay to detect high-grade cervical intraepithelial neoplasia

Objective To evaluate the reproducibility and accuracy of the HPV16/18-E6 test. Methods The study population was comprised of 448 women with a previously abnormal Pap who were referred to the Barretos Cancer Hospital (Brazil) for diagnosis and treatment. Two cervical samples were collected immediately before colposcopy, one for the hr-HPV-DNA test and cytology and the other for the HPV16/18-E6 test using high-affinity monoclonal antibodies (mAb). Women with a histologic diagnosis of cervical intraepithelial neoplasia grade 2 or 3 were considered to be positive cases. Different strategies using a combination of screening methods (HPV-DNA) and triage tests (cytology and HPV16/18-E6) were also examined and compared. Results The HPV16/18-E6 test exhibited a lower positivity rate compared with the HPV-DNA test (19.0% vs. 29.3%, p<0.001) and a moderate/high agreement (kappa = 0.68, 95% CI: 0.60-0.75). It also exhibited a significantly lower sensitivity for CIN2+ and CIN3+ detection compared to the HPV-DNA test and a significantly higher specificity. The HPV16/18-E6 test was no different from cytology in terms of sensitivity, but it exhibited a significantly higher specificity in comparison to ASCH+. A triage test after HPV-DNA detection using the HPV16/18-E6 test exhibited a significantly higher specificity compared with a triage test of ASCH+ to CIN2+ (91.8% vs. 87.4%, p = 0.04) and CIN3+ (88.6% vs. 84.0%, p = 0.05). Conclusion The HPV16/18-E6 test exhibited moderate/high agreement with the HPV-DNA test but lower sensitivity and higher specificity for the detection of CIN2+ and CIN3+. In addition, its performance was quite similar to cytology, but because of the structural design addressed for the detection of HPV16/18-E6 protein, the test can miss some CIN2/3+ lesions caused by other high-risk HPV types.Cancer Prevention Department, Center for the Researcher Support and Pathology Department of the Barretos Cancer Hospital. This study was supported by CNPq 573799/2008-3 and FAPESP 2008/57889-1info:eu-repo/semantics/publishedVersio

Active Learning for Patch-Based Digital Pathology using Convolutional Neural Networks to Reduce Annotation Costs

Methods to reduce the need for costly data annotations become increasingly important as deep learning gains popularity in medical image analysis and digital pathology. Active learning is an appealing approach that can reduce the amount of annotated data needed to train machine learning models but traditional active learning strategies do not always work well with deep learning. In patch-based machine learning systems, active learning methods typically request annotations for small individual patches which can be tedious and costly for the annotator who needs to rely on visual context for the patches. We propose an active learning framework that selects regions for annotation that are built up of several patches, which should increase annotation throughput. The framework was evaluated with several query strategies on the task of nuclei classification. Convolutional neural networks were trained on small patches, each containing a single nucleus. Traditional query strategies performed worse than random sampling. A K-centre sampling strategy showed a modest gain. Further investigation is needed in order to achieve significant performance gains using deep active learning for this task

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