The impacts of collaboration between local health care and non-health care organizations and factors shaping how they work: a systematic review of reviews.

BACKGROUND: Policymakers in many countries promote collaboration between health care organizations and other sectors as a route to improving population health. Local collaborations have been developed for decades. Yet little is known about the impact of cross-sector collaboration on health and health equity. METHODS: We carried out a systematic review of reviews to synthesize evidence on the health impacts of collaboration between local health care and non-health care organizations, and to understand the factors affecting how these partnerships functioned. We searched four databases and included 36 studies (reviews) in our review. We extracted data from these studies and used Nvivo 12 to help categorize the data. We assessed risk of bias in the studies using standardized tools. We used a narrative approach to synthesizing and reporting the data. RESULTS: The 36 studies we reviewed included evidence on varying forms of collaboration in diverse contexts. Some studies included data on collaborations with broad population health goals, such as preventing disease and reducing health inequalities. Others focused on collaborations with a narrower focus, such as better integration between health care and social services. Overall, there is little convincing evidence to suggest that collaboration between local health care and non-health care organizations improves health outcomes. Evidence of impact on health services is mixed. And evidence of impact on resource use and spending are limited and mixed. Despite this, many studies report on factors associated with better or worse collaboration. We grouped these into five domains: motivation and purpose, relationships and cultures, resources and capabilities, governance and leadership, and external factors. But data linking factors in these domains to collaboration outcomes is sparse. CONCLUSIONS: In theory, collaboration between local health care and non-health care organizations might contribute to better population health. But we know little about which kinds of collaborations work, for whom, and in what contexts. The benefits of collaboration may be hard to deliver, hard to measure, and overestimated by policymakers. Ultimately, local collaborations should be understood within their macro-level political and economic context, and as one component within a wider system of factors and interventions interacting to shape population health

Identification of single-site gold catalysis in acetylene hydrochlorination

There remains considerable debate over the active form of gold under operating conditions of a recently validated gold catalyst for acetylene hydrochlorination. We have performed an in situ x-ray absorption fine structure study of gold/carbon (Au/C) catalysts under acetylene hydrochlorination reaction conditions and show that highly active catalysts comprise single-site cationic Au entities whose activity correlates with the ratio of Au(I):Au(III) present. We demonstrate that these Au/C catalysts are supported analogs of single-site homogeneous Au catalysts and propose a mechanism, supported by computational modeling, based on a redox couple of Au(I)-Au(III) species. View Full Tex

The transcriptional repressor protein NsrR senses nitric oxide directly via a [2Fe-2S] cluster

The regulatory protein NsrR, a member of the Rrf2 family of transcription repressors, is specifically dedicated to sensing nitric oxide (NO) in a variety of pathogenic and non-pathogenic bacteria. It has been proposed that NO directly modulates NsrR activity by interacting with a predicted [Fe-S] cluster in the NsrR protein, but no experimental evidence has been published to support this hypothesis. Here we report the purification of NsrR from the obligate aerobe Streptomyces coelicolor. We demonstrate using UV-visible, near UV CD and EPR spectroscopy that the protein contains an NO-sensitive [2Fe-2S] cluster when purified from E. coli. Upon exposure of NsrR to NO, the cluster is nitrosylated, which results in the loss of DNA binding activity as detected by bandshift assays. Removal of the [2Fe-2S] cluster to generate apo-NsrR also resulted in loss of DNA binding activity. This is the first demonstration that NsrR contains an NO-sensitive [2Fe-2S] cluster that is required for DNA binding activity

Heterogenisation of ketone catalysts within mesoporous supports for asymmetric epoxidation

The synthesis of the first mesoporous silica (150 Å) anchored carbohydrate-derived chiral ketone is described. This new heterogeneous catalyst has been shown to be effective in the asymmetric epoxidation of olefins by oxone. The heterogeneous ketone catalyst has comparable activity to that of its homogeneous counterpart and returned enantioselectivities up to 90% e.e

Detecting SARS-CoV-2 variants with SNP genotyping.

Tracking genetic variations from positive SARS-CoV-2 samples yields crucial information about the number of variants circulating in an outbreak and the possible lines of transmission but sequencing every positive SARS-CoV-2 sample would be prohibitively costly for population-scale test and trace operations. Genotyping is a rapid, high-throughput and low-cost alternative for screening positive SARS-CoV-2 samples in many settings. We have designed a SNP identification pipeline to identify genetic variation using sequenced SARS-CoV-2 samples. Our pipeline identifies a minimal marker panel that can define distinct genotypes. To evaluate the system, we developed a genotyping panel to detect variants-identified from SARS-CoV-2 sequences surveyed between March and May 2020 and tested this on 50 stored qRT-PCR positive SARS-CoV-2 clinical samples that had been collected across the South West of the UK in April 2020. The 50 samples split into 15 distinct genotypes and there was a 61.9% probability that any two randomly chosen samples from our set of 50 would have a distinct genotype. In a high throughput laboratory, qRT-PCR positive samples pooled into 384-well plates could be screened with a marker panel at a cost of < £1.50 per sample. Our results demonstrate the usefulness of a SNP genotyping panel to provide a rapid, cost-effective, and reliable way to monitor SARS-CoV-2 variants circulating in an outbreak. Our analysis pipeline is publicly available and will allow for marker panels to be updated periodically as viral genotypes arise or disappear from circulation

Detecting SARS-CoV-2 variants with SNP genotyping.

Tracking genetic variations from positive SARS-CoV-2 samples yields crucial information about the number of variants circulating in an outbreak and the possible lines of transmission but sequencing every positive SARS-CoV-2 sample would be prohibitively costly for population-scale test and trace operations. Genotyping is a rapid, high-throughput and low-cost alternative for screening positive SARS-CoV-2 samples in many settings. We have designed a SNP identification pipeline to identify genetic variation using sequenced SARS-CoV-2 samples. Our pipeline identifies a minimal marker panel that can define distinct genotypes. To evaluate the system, we developed a genotyping panel to detect variants-identified from SARS-CoV-2 sequences surveyed between March and May 2020 and tested this on 50 stored qRT-PCR positive SARS-CoV-2 clinical samples that had been collected across the South West of the UK in April 2020. The 50 samples split into 15 distinct genotypes and there was a 61.9% probability that any two randomly chosen samples from our set of 50 would have a distinct genotype. In a high throughput laboratory, qRT-PCR positive samples pooled into 384-well plates could be screened with a marker panel at a cost of < £1.50 per sample. Our results demonstrate the usefulness of a SNP genotyping panel to provide a rapid, cost-effective, and reliable way to monitor SARS-CoV-2 variants circulating in an outbreak. Our analysis pipeline is publicly available and will allow for marker panels to be updated periodically as viral genotypes arise or disappear from circulation

Comparing methods of measuring sea-ice density in the East Antarctic

Remotely sensed derivation of sea-ice thickness requires sea·ice density. Sea-ice density was estimated with three techniques during the second Sea Ice Physics and Ecosystem eXperiment (SIPEX-II, September-November 2012, East Antarctica). The sea ice was first-year highly deformed, mean thickness 1.2 m with layers, consistent with rafting, and 6-7/10 columnar ice and 3/10 granular ice. Ice density was found to be lower than values (900-920 kg m⁻³ used previously to derive ice thickness, with columnar ice mean density of 870 kg m⁻³. At two different ice stations the mean density of the ice was 870 and 800 kg m⁻³, the lower density reflecting a high percentage of porous granular ice at the second station. Error estimates for mass/volume and liquid/solid water methods are presented. With 0.1 m long, 0.1 m core samples, the error on individual density estimates is 28 kg m⁻³. Errors are larger for smaller machined blocks. Errors increase to 46 kg m⁻³ if the liquid/solid volume method is used. The mass/volume method has a low bias due to brine drainage of at least 5%. Bulk densities estimated from ice and snow measurements along 100 m transects were high, and likely unrealistic as the assumption of isostatic balance is not suitable over these length scales in deformed ice

Photometric redshifts and quasar probabilities from a single, data-driven generative model

We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally incorporate flux uncertainties, missing data, and multi-wavelength photometry. We build models of quasars in flux-redshift space by applying the extreme deconvolution technique to estimate the underlying density. By integrating this density over redshift one can obtain quasar flux-densities in different redshift ranges. This approach allows for efficient, consistent, and fast classification and photometric redshift estimation. This is achieved by combining the speed obtained by choosing simple analytical forms as the basis of our density model with the flexibility of non-parametric models through the use of many simple components with many parameters. We show that this technique is competitive with the best photometric quasar classification techniques---which are limited to fixed, broad redshift ranges and high signal-to-noise ratio data---and with the best photometric redshift techniques when applied to broadband optical data. We demonstrate that the inclusion of UV and NIR data significantly improves photometric quasar--star separation and essentially resolves all of the redshift degeneracies for quasars inherent to the ugriz filter system, even when included data have a low signal-to-noise ratio. For quasars spectroscopically confirmed by the SDSS 84 and 97 percent of the objects with GALEX UV and UKIDSS NIR data have photometric redshifts within 0.1 and 0.3, respectively, of the spectroscopic redshift; this amounts to about a factor of three improvement over ugriz-only photometric redshifts. Our code to calculate quasar probabilities and redshift probability distributions is publicly available