Human capabilities of South African parents who have children with developmental disabilities

Background: Parenting a child with a developmental disability (DD) has a substantial influence on the lives of the parents or caregivers, as well as on how the family operates. This is frequently because of the adjustments in some daily practices that are crucial for parents’ or caregivers’ human capabilities to provide for childcare. There is not enough research done on human capabilities of parents or children with DD in South Africa. Objectives: This study investigated the available support in improving the human capabilities of parents or caregivers with children with DD and the bodily health and bodily integrity human capabilities of parents or caregivers with children with DD. Method: Qualitative interviews were conducted with 11 parents or caregivers of children aged between 1 and 8 years old with DD. This study used snowball sampling. Thematic data analysis was chosen to analyse the data collected. Results: The results of the study indicate that participants have difficulties bringing up their children because of the emotional strain that goes along with parenting a child with DD. In addition, participants were not able to afford decent and satisfactory shelter and had limited access to good quality food because they could not afford it

Parental Understanding of Mental Health in Early Childhood Development: A Human Capabilities Approach

Background: the family is central to a child’s development and well-being. Parental understanding of mental health or psychological distress have a significant effect on child development including their mental health. The aim of this qualitative study was to explore parents understanding of mental health in early childhood development. Methods: semi-structured interviews with 12 parents of children in early childhood development were conducted. Five themes emerged from the thematic analysis: (1) Understanding mental health; (2) Perceptions of parental mental health in relation to the child; (3) Child’s behaviour when experiencing different emotions; (4) Child’s behaviour when witnessing parent’s emotions; and (5) Child’s interaction, confidence, and attitude. Overall, parents were not clear in understanding mental health. They could understand and identify basic emotions but were unable to identify early childhood mental health signs. Recommendations enhancing the knowledge and skills of parents are provided within the capabilities framework

Adaptable image quality assessment using meta-reinforcement learning of task amenability

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7 % % and 29.6 % % expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100 % % expert labels

Open Problems on Central Simple Algebras

We provide a survey of past research and a list of open problems regarding central simple algebras and the Brauer group over a field, intended both for experts and for beginners.Comment: v2 has some small revisions to the text. Some items are re-numbered, compared to v

Does the Mediterranean diet predict longevity in the elderly? A Swedish perspective

Dietary pattern analysis represents a useful improvement in the investigation of diet and health relationships. Particularly, the Mediterranean diet pattern has been associated with reduced mortality risk in several studies involving both younger and elderly population groups. In this research, relationships between dietary macronutrient composition, as well as the Mediterranean diet, and total mortality were assessed in 1,037 seventy-year-old subjects (540 females) information. Diet macronutrient composition was not associated with mortality, while a refined version of the modified Mediterranean diet index showed a significant inverse association (HR = 0.93, 95% CI: 0.89; 0.98). As expected, inactive subjects, smokers and those with a higher waist circumference had a higher mortality, while a reduced risk characterized married and more educated people. Sensitivity analyses (which confirmed our results) consisted of: exclusion of one food group at a time in the Mediterranean diet index, exclusion of early deaths, censoring at fixed follow-up time, adjusting for activities of daily living and main cardiovascular risk factors including weight/waist circumference changes at follow up. In conclusion, we can reasonably state that a higher adherence to a Mediterranean diet pattern, especially by consuming wholegrain cereals, foods rich in polyunsaturated fatty acids, and a limited amount of alcohol, predicts increased longevity in the elderly

Electronic patient self-assessment and management (SAM): a novel framework for cancer survivorship

<p>Abstract</p> <p>Background</p> <p>We propose a novel framework for management of cancer survivorship: electronic patient Self-Assessment and Management (SAM). SAM is a framework for transfer of information to and from patients in such a way as to increase both the patient's and the health care provider's understanding of the patient's progress, and to help ensure that patient care follows best practice.</p> <p>Methods</p> <p>Patients who participate in the SAM system are contacted by email at regular intervals and asked to complete validated questionnaires online. Patient responses on these questionnaires are then analyzed in order to provide patients with real-time, online information about their progress and to provide them with tailored and standardized medical advice. Patient-level data from the questionnaires are ported in real time to the patient's health care provider to be uploaded to clinic notes. An initial version of SAM has been developed at Memorial Sloan-Kettering Cancer Center (MSKCC) and the University of California, San Francisco (UCSF) for aiding the clinical management of patients after surgery for prostate cancer.</p> <p>Results</p> <p>Pilot testing at MSKCC and UCSF suggests that implementation of SAM systems are feasible, with no major problems with compliance (> 70% response rate) or security.</p> <p>Conclusion</p> <p>SAM is a conceptually simple framework for passing information to and from patients in such a way as to increase both the patient's and the health care provider's understanding of the patient's progress, and to help ensure that patient care follows best practice.</p

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

Location determinants of green technological entry: evidence from European regions

In this paper, we explore the spatial distribution and the location determinants of new green technology-based firms across European regions. Integrating insights from evolutionary economic geography and the literature on knowledge spillovers, we study the importance of new knowledge creation and the conditioning role played by regional technological relatedness in fostering combinatorial opportunities underlying the process of green technological entry. The analysis is based on a dataset covering over 900 NUTS3 regions for 15 European countries obtained merging economic data from ESPON-Eurostat and patent information from the PATSTAT-CRIOS database for the period 1996–2006. Our results show that the geographical distribution of green technological entry across European regions is not evenly distributed, offering evidence of spatial path dependence. In line with this, we find evidence of a significant role played by the characteristics of the regional innovation system. New green innovators are more likely to develop in regions defined by higher levels of technological activity underlying knowledge spillovers and more dynamism in technological entry. Moreover, our findings point to an inverted-U relationship between regional technological relatedness and green technological entry. Regions whose innovation activity is defined by cognitive proximity to environmental technologies support interactive learning and knowledge spillovers underlying entrepreneurship in this specific area. However, too much relatedness may cause technological lock-ins and reduce the set of combinatorial opportunities

Does true Gleason pattern 3 merit its cancer descriptor?

Nearly five decades following its conception, the Gleason grading system remains a cornerstone in the prognostication and management of patients with prostate cancer. In the past few years, a debate has been growing whether Gleason score 3 + 3 = 6 prostate cancer is a clinically significant disease. Clinical, molecular and genetic research is addressing the question whether well characterized Gleason score 3 + 3 = 6 disease has the ability to affect the morbidity and quality of life of an individual in whom it is diagnosed. The consequences of treatment of Gleason score 3 + 3 = 6 disease are considerable; few men get through their treatments without sustaining some harm. Further modification of the classification of prostate cancer and dropping the label cancer for Gleason score 3 + 3 = 6 disease might be warranted