Developing sustainability in a dental practice through an Action Research approach

Introduction: For dental practices to become more environmentally sustainable, the full cooperation of all practice staff can lead to greater engagement with proposed initiatives. Action Research is a participatory approach to change that involves participants in the decisions that affect them. Aim: The aim of this study was to collaboratively develop interventions to improve environmental sustainability at one dental practice. Methods: This was a mixed methods study following an Action Research process for intervention design. The study was set in a mixed NHS / private dental practice in England, UK. Results: Participants engaged in the process and were able to choose the measures they wished to implement. Feedback showed that whilst some measures had been discarded, others had led to changes both in professional and personal behaviour. Conclusion: Action Research offers an innovative and constructive method of engaging participants in developing their own sustainability strategies and can encourage positive change

On the Inverse Problem of Binocular 3D Motion Perception

It is shown that existing processing schemes of 3D motion perception such as interocular velocity difference, changing disparity over time, as well as joint encoding of motion and disparity, do not offer a general solution to the inverse optics problem of local binocular 3D motion. Instead we suggest that local velocity constraints in combination with binocular disparity and other depth cues provide a more flexible framework for the solution of the inverse problem. In the context of the aperture problem we derive predictions from two plausible default strategies: (1) the vector normal prefers slow motion in 3D whereas (2) the cyclopean average is based on slow motion in 2D. Predicting perceived motion directions for ambiguous line motion provides an opportunity to distinguish between these strategies of 3D motion processing. Our theoretical results suggest that velocity constraints and disparity from feature tracking are needed to solve the inverse problem of 3D motion perception. It seems plausible that motion and disparity input is processed in parallel and integrated late in the visual processing hierarchy

Rescue of Dystrophic Skeletal Muscle by PGC-1α Involves a Fast to Slow Fiber Type Shift in the mdx Mouse

Increased utrophin expression is known to reduce pathology in dystrophin-deficient skeletal muscles. Transgenic over-expression of PGC-1α has been shown to increase levels of utrophin mRNA and improve the histology of mdx muscles. Other reports have shown that PGC-1α signaling can lead to increased oxidative capacity and a fast to slow fiber type shift. Given that it has been shown that slow fibers produce and maintain more utrophin than fast skeletal muscle fibers, we hypothesized that over-expression of PGC-1α in post-natal mdx mice would increase utrophin levels via a fiber type shift, resulting in more slow, oxidative fibers that are also more resistant to contraction-induced damage. To test this hypothesis, neonatal mdx mice were injected with recombinant adeno-associated virus (AAV) driving expression of PGC-1α. PGC-1α over-expression resulted in increased utrophin and type I myosin heavy chain expression as well as elevated mitochondrial protein expression. Muscles were shown to be more resistant to contraction-induced damage and more fatigue resistant. Sirt-1 was increased while p38 activation and NRF-1 were reduced in PGC-1α over-expressing muscle when compared to control. We also evaluated if the use a pharmacological PGC-1α pathway activator, resveratrol, could drive the same physiological changes. Resveratrol administration (100 mg/kg/day) resulted in improved fatigue resistance, but did not achieve significant increases in utrophin expression. These data suggest that the PGC-1α pathway is a potential target for therapeutic intervention in dystrophic skeletal muscle

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease