Interferometric Mapping of Magnetic fields: NGC2071IR

We present polarization maps of NGC2071IR from thermal dust emission at 1.3 mm and from CO J=$2 \to 1$ line emission. The observations were obtained using the Berkeley-Illinois-Maryland Association array in the period 2002-2004. We detected dust and line polarized emission from NGC2071IR that we used to constrain the morphology of the magnetic field. From CO J=$2 \to 1$ polarized emission we found evidence for a magnetic field in the powerful bipolar outflow present in this region. We calculated a visual extinction $A_{\rm{v}} \approx 26$ mag from our dust observations. This result, when compared with early single dish work, seems to show that dust grains emit polarized radiation efficiently at higher densities than previously thought. Mechanical alignment by the outflow is proposed to explain the polarization pattern observed in NGC2071IR, which is consistent with the observed flattening in this source.Comment: 17 pages, 4 Figures, Accepted for publication in Ap

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

Ecosystem Services in Decision Making: Time to Deliver

Over the past decade, efforts to value and protect ecosystem services have been promoted by many as the last, best hope for making conservation mainstream – attractive and commonplace worldwide. In theory, if we can help individuals and institutions to recognize the value of nature, then this should greatly increase investments in conservation, while at the same time fostering human well-being. In practice, however, we have not yet developed the scientific basis, nor the policy and finance mechanisms, for incorporating natural capital into resource- and land-use decisions on a large scale. Here, we propose a conceptual framework and sketch out a strategic plan for delivering on the promise of ecosystem services, drawing on emerging examples from Hawai‘i. We describe key advances in the science and practice of accounting for natural capital in the decisions of individuals, communities, corporations, and governments

Ecosystem Services in Decision Making: Time to Deliver

Over the past decade, efforts to value and protect ecosystem services have been promoted by many as the last, best hope for making conservation mainstream – attractive and commonplace worldwide. In theory, if we can help individuals and institutions to recognize the value of nature, then this should greatly increase investments in conservation, while at the same time fostering human well-being. In practice, however, we have not yet developed the scientific basis, nor the policy and finance mechanisms, for incorporating natural capital into resource- and land-use decisions on a large scale. Here, we propose a conceptual framework and sketch out a strategic plan for delivering on the promise of ecosystem services, drawing on emerging examples from Hawai‘i. We describe key advances in the science and practice of accounting for natural capital in the decisions of individuals, communities, corporations, and governments

Milestones: a mixed methods study of an educational intervention to improve care of the dying

Background: Approximately 460 000 people die annually in England. Three-quarters of these deaths are expected. Health Education England is prioritising upskilling of clinical staff in response to reports of poor care quality in the last days of life in acute hospitals, where almost half of all deaths occur. This study explores the impact of an end-of-life care (EoLC) educational intervention, Milestones, in acute hospital trusts in Greater London. Methods: This is a mixed methods study. Learners completed a questionnaire pre- (n=452), immediately post- (n=488) and 3 to 8 months post- (n=37) intervention. The questionnaire measured learner confidence in EoLC covering the National Health Service adopted ‘Priorities for the Care of the Dying Person’. Paired t-tests were used to determine statistically significant difference in learner confidence pre- and post-intervention. A convenience sample of learners (n=7) and educators (n=5) were recruited to qualitative semi-structured interviews that sought to understand if, how and why Milestones worked. Data were analysed using a thematic approach. Results: A statistically significant increase in learner confidence across all five priorities of care’ was sustained up to 8 months (p<0.001). Interviewees wanted to discuss wider challenges in EoLC related to the organisations and cultural contexts in which they worked. Concerns included balancing hope when decision-making, learning as a multidisciplinary team and emotional impact. Conclusion: The findings suggest that Milestones is a flexible, beneficial resource for teaching EoLC that facilitates enhanced learner engagement. Understanding generated about wider concerns can inform future educational material development, organisational process and research study design

The rate of brain abnormalities on in utero MRI studies in fetuses with normal ultrasound examinations of the brain and calculation of indicators of diagnostic performance

AIM To estimate the rate of unexpected brain abnormalities detected by in utero magnetic resonance imaging (iuMRI) in fetuses without abnormalities at ultrasonography (USS). MATERIALS AND METHODS A prospective cohort study of pregnant women whose fetus had no structural brain (or body) abnormalities recognised on antenatal ultrasonography. Women were recruited from 12 centres across the UK and underwent iuMRI at 18 gestational weeks or more in the [blinded for review]. The imaging studies were reviewed by an experienced neuroradiologist. The positive and negative predictive values of both USS and iuMRI have been calculated by combining the results of this study with the results from the main [blinded for review] study. RESULTS One hundred and ninety-eight pregnant women were recruited and underwent iuMRI of 205 fetuses. Brain abnormalities were shown on iuMRI in two fetuses that were not recognised on USS (one case of a focal cortical abnormality and one case of mild ventriculomegaly). The negative predictive value for USS was 99.5% and 100% for iuMRI. CONCLUSIONS To the authors' knowledge, this is the first study comparing USS and iuMRI in low-risk pregnancies. USS has a comparatively high rule-out for fetal brain abnormalities and should remain the screening tool of choice

The role of drug resistance in poor viral suppression in rural South Africa: findings from a population-based study.

BACKGROUND:Understanding factors driving virological failure, including the contribution of HIV drug resistance mutations (DRM), is critical to ensuring HIV treatment remains effective. We examine the contribution of drug resistance mutations for low viral suppression in HIV-positive participants in a population-based sero-prevalence survey in rural South Africa. METHODS:We conducted HIV drug resistance genotyping and ART analyte testing on dried blood spots (DBS) from HIV-positive adults participating in a 2014 survey in North West Province. Among those with virologic failure (&gt; 5000 copies/mL), we describe frequency of DRM to protease inhibitors (PI), nucleoside reverse transcriptase inhibitors (NRTI), and non-nucleoside reverse transcriptase inhibitors (NNRTI), report association of resistance with antiretroviral therapy (ART) status, and assess resistance to first and second line therapy. Analyses are weighted to account for sampling design. RESULTS:Overall 170 DBS samples were assayed for viral load and ART analytes; 78.4% of men and 50.0% of women had evidence of virologic failure and were assessed for drug resistance, with successful sequencing of 76/107 samples. We found ≥1 DRM in 22% of participants; 47% were from samples with detectable analyte (efavirenz, nevirapine or lopinavir). Of those with DRM and detectable analyte, 60% showed high-level resistance and reduced predicted virologic response to ≥1 NRTI/NNRTI typically used in first and second-line regimens. CONCLUSIONS:DRM and predicted reduced susceptibility to first and second-line regimens were common among adults with ART exposure in a rural South African population-based sample. Results underscore the importance of ongoing virologic monitoring, regimen optimization and adherence counseling to optimize durable virologic suppression

Single and Composite Hot Subdwarf Stars in the Light of 2MASS Photometry

Utilizing the Two Micron All Sky Survey (2MASS) Second Incremental Data Release Catalog, we have retrieved near-IR magnitudes for several hundred hot subdwarfs (sdO and sdB stars) drawn from the "Catalogue of Spectroscopically Identified Hot Subdwarfs" (Kilkenny, Heber, & Drilling 1988, 1992). This sample size greatly exceeds that of previous studies of hot subdwarfs. Examining 2MASS photometry alone or in combination with visual photometry (Johnson BV or Stromgren uvby) available in the literature, we show that it is possible to identify hot subdwarf stars that exhibit atypically red IR colors that can be attributed to the presence of an unresolved late type companion. Utilizing this large sample, we attempt for the first time to define an approximately volume limited sample of hot subdwarfs. We discuss the considerations, biases, and difficulties in defining such a sample. We find that, of the hot subdwarfs in Kilkenny et al., about 40% in a magnitude limited sample have colors that are consistent with the presence of an unresolved late type companion. Binary stars are over-represented in a magnitude limited sample. In an approximately volume limited sample the fraction of composite-color binaries is about 30%.Comment: to appear in Sept 2003 AJ, 41 pages total, 12 figures, 2 tables are truncated (full tables to appear in electronic journal or available by request

Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome

BACKGROUND: Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins. RESULTS: We established two tests of the accuracy of human protein interaction datasets and measured the relative accuracy of the available data. We then developed and applied natural language processing and literature-mining algorithms to recover from Medline abstracts 6,580 interactions among 3,737 human proteins. A three-part algorithm was used: first, human protein names were identified in Medline abstracts using a discriminator based on conditional random fields, then interactions were identified by the co-occurrence of protein names across the set of Medline abstracts, filtering the interactions with a Bayesian classifier to enrich for legitimate physical interactions. These mined interactions were combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing datasets. CONCLUSION: These interactions and the accuracy benchmarks will aid interpretation of current functional genomics data and provide a basis for determining the quality of future large-scale human protein interaction assays. Projecting from the approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies more than 375,000 interactions in the complete human protein interaction network. This set therefore represents no more than 10% of the complete network

How well do we forecast the aurora?

Michaela K Mooney and co-authors evaluate a space weather forecast model in the same way that weather forecasts are assessed, work that won the 2019 Rishbeth Prize for best poster