Communities as ‘renewable energy’ for health care services? A multi-methods study into the form, scale, and role of voluntary support for community hospitals in England

Objective To examine the forms, scale and role of community and voluntary support for community hospitals in England. Design A multi-methods study. Quantitative analysis of Charity Commission data on levels of volunteering and voluntary income for charities supporting community hospitals. Nine qualitative case studies of community hospitals and their surrounding communities, including interviews and focus groups. Setting Community hospitals in England and their surrounding communities. Participants Charity Commission data for 245 community hospital Leagues of Friends. Interviews with staff (89), patients (60), carers (28), volunteers (35), community representatives (20), managers and commissioners (9). Focus groups with multi-disciplinary teams (8 groups across nine sites, involving 43 respondents), volunteers (6 groups, 33 respondents) and community stakeholders (8 groups, 54 respondents). Results Communities support community hospitals through: human resources (average = 24 volunteers a year per hospital); financial resources (median voluntary income = £15,632); practical resources through services and activities provided by voluntary and community groups; and intellectual resources (e.g. consultation and coproduction). Communities provide valuable supplementary resources to the NHS, enhancing community hospital services, patient experience, staff morale and volunteer well-being. Such resources, however, vary in level and form from hospital to hospital and over time: voluntary income is on the decline, as is membership of League of Friends, and it can be hard to recruit regular, active volunteers. Conclusions Communities can be a significant resource for health care services, in ways which can enhance patient experience and service quality. Harnessing that resource, however, is not straight forward and there is a perception that it might be becoming more difficult questioning the extent to which it can be considered sustainable or ‘renewable’

Quantum kernel methods for solving regression problems and differential equations

This is the final version. Available on open access from the American Physical Society via the DPO in this recordWe propose several approaches for solving regression problems and differential equations (DEs) with quantum kernel methods. We compose quantum models as weighted sums of kernel functions, where variables are encoded using feature maps and model derivatives are represented using automatic differentiation of quantum circuits. While previously quantum kernel methods primarily targeted classification tasks, here we consider their applicability to regression tasks, based on available data and differential constraints. We use two strategies to approach these problems. First, we devise a mixed model regression with a trial solution represented by kernel-based functions, which is trained to minimize a loss for specific differential constraints or datasets. Second, we use support vector regression that accounts for the structure of differential equations. The developed methods are capable of solving both linear and nonlinear systems. Contrary to prevailing hybrid variational approaches for parametrized quantum circuits, we perform training of the weights of the model classically. Under certain conditions this corresponds to a convex optimization problem, which can be solved with provable convergence to global optimum of the model. The proposed approaches also favor hardware implementations, as optimization only uses evaluated Gram matrices, but require a quadratic number of function evaluations. We highlight trade-offs when comparing our methods to those based on variational quantum circuits such as the recently proposed differentiable quantum circuits approach. The proposed methods offer potential quantum enhancement through the rich kernel representations using the power of quantum feature maps, and start the quest towards provably trainable quantum DE solvers

Solving nonlinear differential equations with differentiable quantum circuits

This is the final version. Available from the American Physical Society via the DOI in this recordWe propose a quantum algorithm to solve systems of nonlinear differential equations. Using a quantum feature map encoding, we define functions as expectation values of parametrized quantum circuits. We use automatic differentiation to represent function derivatives in an analytical form as differentiable quantum circuits (DQCs), thus avoiding inaccurate finite difference procedures for calculating gradients. We describe a hybrid quantum-classical workflow where DQCs are trained to satisfy differential equations and specified boundary conditions. As a particular example setting, we show how this approach can implement a spectral method for solving differential equations in a high-dimensional feature space. From a technical perspective, we design a Chebyshev quantum feature map that offers a powerful basis set of fitting polynomials and possesses rich expressivity. We simulate the algorithm to solve an instance of Navier-Stokes equations, and compute density, temperature and velocity profiles for the fluid flow in a convergent-divergent nozzle

Quantum quantile mechanics: Solving stochastic differential equations for generating time‐series

This is the final version. Available from Wiley via the DOI in this record. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.A quantum algorithm is proposed for sampling from a solution of stochastic differential equations (SDEs). Using differentiable quantum circuits (DQCs) with a feature map encoding of latent variables, the quantile function is represented for an underlying probability distribution and samples extracted as DQC expectation values. Using quantile mechanics the system is propagated in time, thereby allowing for time-series generation. The method is tested by simulating the Ornstein-Uhlenbeck process and sampling at times different from the initial point, as required in financial analysis and dataset augmentation. Additionally, continuous quantum generative adversarial networks (qGANs) are analyzed, and the authors show that they represent quantile functions with a modified (reordered) shape that impedes their efficient time-propagation. The results shed light on the connection between quantum quantile mechanics (QQM) and qGANs for SDE-based distributions, and point the importance of differential constraints for model training, analogously with the recent success of physics informed neural networks

Analysis of the profile, characteristics, patient experience and community value of community hospitals : a multimethod study

Background: Community hospitals have been part of England’s health-care landscape since the mid-nineteenth century. Evidence on them has not kept pace with their development. Aim: To provide a comprehensive analysis of the profile, characteristics, patient experience and community value of community hospitals. Design: A multimethod study with three phases. Phase one involved national mapping and the construction of a new database of community hospitals through data set reconciliation and verification. Phase two involved nine case studies, including interviews and focus groups with patients (n = 60), carers (n = 28), staff (n = 132), volunteers (n = 68), community stakeholders (n = 74) and managers and commissioners (n = 9). Phase three involved analysis of Charity Commission data on voluntary support. Setting: Community hospitals in England. Results: The study identified 296 community hospitals with beds in England. Typically, the hospitals were small (<30 beds), in rural communities, led by doctors/general practitioners (GPs) and nurses, without 24/7 on-site medical cover, providing step-down and step-up inpatient care, with an average length of stay of <30 days and a variable range of intermediate care services. Key to patients’ and carers’ experiences of community hospitals was their closeness to ‘home’ through their physical location, environment and atmosphere and the relationships that they support; their provision of personalised, holistic care; and their role in supporting patients through difficult psychological transitions. Communities engage with and support their hospitals through giving time (average = 24 volunteers), raising money (median voluntary income = £15,632), providing services (voluntary and community groups) and giving voice (e.g. communication and consultation). This can contribute to hospital utilisation and sustainability, patient experience, staff morale and volunteer well-being. Engagement varies between and within communities and over time. Community hospitals are important community assets, representing direct and indirect value: instrumental (e.g. health care), economic (e.g. employment), human (e.g. skills development), social (e.g. networks), cultural (e.g. identity and belonging) and symbolic (e.g. vitality and security). Value varies depending on place and time. Limitations: There were limitations to the secondary data available for mapping community hospitals and tracking charitable funds and to our sample of case study respondents, which concentrated on people with a connection to the hospitals. Conclusions: Community hospitals are diverse but are united by a set of common characteristics. Patients and carers experience community hospitals as qualitatively different from other settings. Their accounts highlight the importance of considering the functional, interpersonal, social and psychological dimensions of experience. Community hospitals are highly valued by their local communities, as demonstrated through their active involvement as volunteers and donors. Community hospitals enable the provision of local intermediate care services, delivered through an embedded, relational model of care, which generates deep feelings of reassurance. However, current developments, including the withdrawal of GPs, shifts towards step-down care for non-local patients and changing configurations of services, providers and ownership may undermine this. Future work: Comparative studies of patient experience in different settings, longitudinal studies of community support and value, studies into the implications of changes in community hospital function, GP involvement, provider-mix and ownership and international comparative studies could all be undertaken

The relationship between the symptoms of female gonococcal infections and serum progesterone level and the genotypes of Neisseria gonorrhoeae multi-antigen sequence type (NG-MAST) in Wuhan, China

The objective of this investigation was to study the relationship between the symptoms of female gonococcal infections and serum progesterone level and the genotypes of Neisseria gonorrhoeae multi-antigen sequence type (NG-MAST) in Wuhan, China. Eighty-one strains of N. gonorrhoeae were harvested from the vaginal discharge of 975 adult females in Wuhan and were genotyped by using NG-MAST. Serum progesterone (P) and estradiol (E2) levels were measured by radio immunoassay (RIA) in 39 gonorrhea-infected patients with slight symptoms (asymptomatic group) and 42 patients with conspicuous symptoms (symptomatic group). The average levels of serum progesterone in the asymptomatic group were significantly higher than in the symptomatic group (p < 0.05), while no significant difference was found in serum estradiol between the two groups. Of 81 wild-type isolates, 50 NG-MAST sequence types were associated with female infections in Wuhan, and N. gonorrhoeae ST2951, ST735, and ST436 were principally found in asymptomatic patients. ST809 and ST369, however, were mainly detected in asymptomatic female subjects. Gonococcal genetic island (GGI)-positive and GGI-negative strains were found in both the asymptomatic group and the symptomatic group. In females with gonococcal infection, high serum progesterone level is associated with the absence of symptoms, but no association was revealed between genotypes and the presence of symptoms. The GGI bears no relation to the absence of symptoms in the patients

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Field-Caught Permethrin-Resistant Anopheles gambiae Overexpress CYP6P3, a P450 That Metabolises Pyrethroids

Insects exposed to pesticides undergo strong natural selection and have developed various adaptive mechanisms to survive. Resistance to pyrethroid insecticides in the malaria vector Anopheles gambiae is receiving increasing attention because it threatens the sustainability of malaria vector control programs in sub-Saharan Africa. An understanding of the molecular mechanisms conferring pyrethroid resistance gives insight into the processes of evolution of adaptive traits and facilitates the development of simple monitoring tools and novel strategies to restore the efficacy of insecticides. For this purpose, it is essential to understand which mechanisms are important in wild mosquitoes. Here, our aim was to identify enzymes that may be important in metabolic resistance to pyrethroids by measuring gene expression for over 250 genes potentially involved in metabolic resistance in phenotyped individuals from a highly resistant, wild A. gambiae population from Ghana. A cytochrome P450, CYP6P3, was significantly overexpressed in the survivors, and we show that the translated enzyme metabolises both alpha-cyano and non–alpha-cyano pyrethroids. This is the first study to demonstrate the capacity of a P450 identified in wild A. gambiae to metabolise insecticides. The findings add to the understanding of the genetic basis of insecticide resistance in wild mosquito populations

Manipulation of Plant Defense Responses by the Tomato Psyllid (Bactericerca cockerelli) and Its Associated Endosymbiont Candidatus Liberibacter Psyllaurous

Some plant pathogens form obligate relationships with their insect vector and are vertically transmitted via eggs analogous to insect endosymbionts. Whether insect endosymbionts manipulate plant defenses to benefit their insect host remains unclear. The tomato psyllid, Bactericerca cockerelli (Sulc), vectors the endosymbiont “Candidatus Liberibacter psyllaurous” (Lps) during feeding on tomato (Solanum lycopersicum L.). Lps titer in psyllids varied relative to the psyllid developmental stage with younger psyllids harboring smaller Lps populations compared to older psyllids. In the present study, feeding by different life stages of B. cockerelli infected with Lps, resulted in distinct tomato transcript profiles. Feeding by young psyllid nymphs, with lower Lps levels, induced tomato genes regulated by jasmonic acid (JA) and salicylic acid (SA) (Allene oxide synthase, Proteinase inhibitor 2, Phenylalanine ammonia-lyase 5, Pathogenesis-related protein 1) compared to feeding by older nymphs and adults, where higher Lps titers were found. In addition, inoculation of Lps without insect hosts suppressed accumulation of these defense transcripts. Collectively, these data suggest that the endosymbiont-like pathogen Lps manipulates plant signaling and defensive responses to benefit themselves and the success of their obligate insect vector on their host plant

Free energies of binding of R- and S-propranolol to wild-type and F483A mutant cytochrome P450 2D6 from molecular dynamics simulations

Detailed molecular dynamics (MD) simulations have been performed to reproduce and rationalize the experimental finding that the F483A mutant of CYP2D6 has lower affinity for R-propranolol than for S-propranolol. Wild-type (WT) CYP2D6 does not show this stereospecificity. Four different approaches to calculate the free energy differences have been investigated and were compared to the experimental binding data. From the differences between calculations based on forward and backward processes and the closure of thermodynamic cycles, it was clear that not all simulations converged sufficiently. The approach that calculates the free energies of exchanging R-propranolol with S-propranolol in the F483A mutant relative to the exchange free energy in WT CYP2D6 accurately reproduced the experimental binding data. Careful inspection of the end-points of the MD simulations involved in this approach, allowed for a molecular interpretation of the observed differences