It\u27s quite good fun: A qualitative study of a singing/songwriting programme for people with Parkinson\u27s disease and their spouses

Background: : Group singing for people with Parkinson\u27s disease (PD) is an established intervention not only to improve voice and speech difficulties but also for emotional and social benefit. Less is known about the experiences of group singing on the couple—the person with PD and their spouse or partner together—and studies have not specifically tracked impact through time or in combination with songwriting. Aims: : To understand the impact of group singing/songwriting on couples (participants with PD and their spouses) to unpack whether this broader view might help explain why such interventions are reported as beneficial. Using a trajectory approach, a form of longitudinal research and focused ethnography, the research sought a deeper appreciation of participation through time for the couple in a singing/songwriting group. Methods & Procedures: : Four couples attending a singing/songwriting programme were observed for 10 weeks, and interviewed formally and informally weekly. Data were analysed thematically across-case through framework analysis but also within-case to explore the couples’ experiences and narratives over time. Outcomes & Results: : The theme of ‘improved relationships’ between the couples was new and extended previous studies’ findings of positivity, physical benefit, sense of self and social opportunity. The stories of each couple highlighted the importance of musical reminiscence and emotional respite, and demonstrated changes with time through the singing and songwriting group. Conclusions & Implications: : The benefits of offering singing/songwriting groups may be felt not only by participants with PD but also by their spouses/partners even if they choose not to attend themselves. Such benefits may include improved relationships related to the shared joy of music, musical reminiscence and emotional respite. The addition of songwriting encourages creativity and agency. A longitudinal trajectory approach is one way to appreciate how these benefits may unfold over time for participants. WHAT THIS PAPER ADDS: What is already known on the subject Group singing for people with PD has been shown to have physical, emotional and social benefits as measured on mainly pre-post-assessments of vocal, speech, respiratory function and quality of life questionnaires. What this study adds to the existing knowledge This study adds three new aspects: studying the benefits for the couple (both people with PD and their spouse/partner); taking an in-depth focused ethnographic approach over time to collect couples’ narratives and experiences; and exploring the potential for adding songwriting to the intervention. What are the potential or actual clinical implications of this work? A qualitative trajectory approach may help clinicians understand why such interventions are experienced as beneficial. Clinicians running singing groups for people with PD should offer attendance to spouses/partners because of the potential for such groups to improve relationships and build new points of connection for the couple, as well as provide peer support for spouses. Songwriting is a useful addition for creativity, cognitive flexibility and self-expression

Parallel Five-Cycle Counting Algorithms

Counting the frequency of subgraphs in large networks is a classic research question that reveals the underlying substructures of these networks for important applications. However, subgraph counting is a challenging problem, even for subgraph sizes as small as five, due to the combinatorial explosion in the number of possible occurrences. This paper focuses on the five-cycle, which is an important special case of five-vertex subgraph counting and one of the most difficult to count efficiently. We design two new parallel five-cycle counting algorithms and prove that they are work-efficient and achieve polylogarithmic span. Both algorithms are based on computing low out-degree orientations, which enables the efficient computation of directed two-paths and three-paths, and the algorithms differ in the ways in which they use this orientation to eliminate double-counting. We develop fast multicore implementations of the algorithms and propose a work scheduling optimization to improve their performance. Our experiments on a variety of real-world graphs using a 36-core machine with two-way hyper-threading show that our algorithms achieves 10-46x self-relative speed-up, outperform our serial benchmarks by 10-32x, and outperform the previous state-of-the-art serial algorithm by up to 818x

Atomic mutagenesis of stop codon nucleotides reveals the chemical prerequisites for release factor-mediated peptide release.

Termination of protein synthesis is triggered by the recognition of a stop codon at the ribosomal A site and is mediated by class I release factors (RFs). Whereas in bacteria, RF1 and RF2 promote termination at UAA/UAG and UAA/UGA stop codons, respectively, eukaryotes only depend on one RF (eRF1) to initiate peptide release at all three stop codons. Based on several structural as well as biochemical studies, interactions between mRNA, tRNA, and rRNA have been proposed to be required for stop codon recognition. In this study, the influence of these interactions was investigated by using chemically modified stop codons. Single functional groups within stop codon nucleotides were substituted to weaken or completely eliminate specific interactions between the respective mRNA and RFs. Our findings provide detailed insight into the recognition mode of bacterial and eukaryotic RFs, thereby revealing the chemical groups of nucleotides that define the identity of stop codons and provide the means to discriminate against noncognate stop codons or UGG sense codons

Diet-induced metabolic dysregulation in female mice causes osteopenia in adult offspring

Bone mass and quality in humans are controlled by numerous genetic and environmental factors that are not fully understood. Increasing evidence has indicated that maternal metabolic dysregulation impairs multiple physiological processes in the adult offspring, but a similar effect on bone health is yet to be established. Here, we have analyzed the bones of first-generation offspring from murine dams that present metabolic syndrome due to a high-fat and high-sugar (HF/HS) diet. Micro-CT analyses show that the long bones of HF/HS offspring possess lower cortical bone mass and weaker mechanical strength than normal, even though the trabecular bone is not affected. Histomorphometry and serum biochemistry indicate that both bone formation and resorption are diminished in the HF/HS offspring. In vitro, both osteoblast and osteoclast progenitors from the HF/HS offspring are deficient in differentiation, likely due to impairment of mitochondrial respiration. The study, therefore, identifies maternal metabolic health as an important environmental factor influencing bone volume and strength

Differential Privacy from Locally Adjustable Graph Algorithms: kk-Core Decomposition, Low Out-Degree Ordering, and Densest Subgraphs

Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that model various (sensitive) relationships between individuals. While there exists a rich history of important literature in this space, to the best of our knowledge, no results formalize a relationship between certain parallel and distributed graph algorithms and differentially private graph analysis. In this paper, we define \emph{locally adjustable} graph algorithms and show that algorithms of this type can be transformed into differentially private algorithms. Our formalization is motivated by a set of results that we present in the central and local models of differential privacy for a number of problems, including $k$-core decomposition, low out-degree ordering, and densest subgraphs. First, we design an $\varepsilon$-edge differentially private (DP) algorithm that returns a subset of nodes that induce a subgraph of density at least $\frac{D^*}{1+\eta} - O\left(\text{poly}(\log n)/\varepsilon\right),$ where $D^*$ is the density of the densest subgraph in the input graph (for any constant $\eta > 0$). This algorithm achieves a two-fold improvement on the multiplicative approximation factor of the previously best-known private densest subgraph algorithms while maintaining a near-linear runtime. Then, we present an $\varepsilon$-locally edge differentially private (LEDP) algorithm for $k$-core decompositions. Our LEDP algorithm provides approximates the core numbers (for any constant $\eta > 0$) with $(2+\eta)$ multiplicative and $O\left(\text{poly}\left(\log n\right)/\varepsilon\right)$ additive error. This is the first differentially private algorithm that outputs private $k$-core decomposition statistics

Ex Vivo Electrochemical Measurement of Glutamate Release During Spinal Cord Injury

Excessive glutamate release following traumatic spinal cord injury (SCI) has been associated with exacerbating the extent of SCI. However, the mechanism behind sustained high levels of extracellular glutamate is unclear. Spinal cord segments mounted in a sucrose double gap recording chamber are an established model for traumatic spinal cord injury. We have developed a method to record, with micro-scale printed glutamate biosensors, glutamate release from ex vivo rat spinal cord segments following injury. This protocol would work equally well for similar glutamate biosensors