Aims: The role of arts and music in supporting subjective wellbeing (SWB) is increasingly recognised. Robust evidence is needed to support policy and practice. This article reports on the first of four reviews of Culture, Sport and Wellbeing (CSW) commissioned by the Economic and Social Research Council (ESRC)-funded What Works Centre for Wellbeing ( https://whatworkswellbeing.org/ ). Objective: To identify SWB outcomes for music and singing in adults. Methods: Comprehensive literature searches were conducted in PsychInfo, Medline, ERIC, Arts and Humanities, Social Science and Science Citation Indexes, Scopus, PILOTS and CINAHL databases. From 5,397 records identified, 61 relevant records were assessed using GRADE and CERQual schema. Results: A wide range of wellbeing measures was used, with no consistency in how SWB was measured across the studies. A wide range of activities was reported, most commonly music listening and regular group singing. Music has been associated with reduced anxiety in young adults, enhanced mood and purpose in adults and mental wellbeing, quality of life, self-awareness and coping in people with diagnosed health conditions. Music and singing have been shown to be effective in enhancing morale and reducing risk of depression in older people. Few studies address SWB in people with dementia. While there are a few studies of music with marginalised communities, participants in community choirs tend to be female, white and relatively well educated. Research challenges include recruiting participants with baseline wellbeing scores that are low enough to record any significant or noteworthy change following a music or singing intervention. Conclusions: There is reliable evidence for positive effects of music and singing on wellbeing in adults. There remains a need for research with sub-groups who are at greater risk of lower levels of wellbeing, and on the processes by which wellbeing outcomes are, or are not, achieved.

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

We report a mutation (Y99C) in guanylate cyclase activator 1A (GUCA1A), the gene for guanylate cyclase activating protein (GCAP1), in a family with autosomal dominant cone dystrophy. Linkage analysis excluded all the known cone and cone-rod dystrophy loci, except the chromosome 6p21.1 region. This is known to contain the RDS gene, which is associated with dominant cone-rod dystrophy. Screening of the RDS gene by heteroduplex analysis and direct sequencing failed to demonstrate sequence changes in the coding region of this gene. The gene for GCAP1, a calcium binding protein which is highly expressed in photoreceptor outer segments, is also located in 6p21.1. It was screened for mutations, and all affected individuals showed a single base pair missense mutation (A→G) at codon 99 in exon 2 of this gene generating a tyrosine-to-cysteine change in the GCAP1 protein. This change was absent from 206 unrelated normal controls. We propose that this change would at least disrupt the EF₃ hand of GCAP1 thereby preventing calcium binding and consequently interfere with activation. The resulting effect on cGMP production would predictably modify the number of open cGMP gated cation channels, and could explain the ultimate demise of cone photoreceptor cells.