Links between surface productivity and deep ocean particle flux at the Porcupine Abyssal Plain sustained observatory

In this study we present hydrography, biogeochemistry and sediment trap observations between 2003 and 2012 at Porcupine Abyssal Plain (PAP) sustained observatory in the Northeast Atlantic. The time series is valuable as it allows for investigation of the link between surface productivity and deep ocean carbon flux. The region is a perennial sink for CO2, with an average uptake of around 1.5 mmol m?2 day?1. The average monthly drawdowns of inorganic carbon and nitrogen were used to quantify the net community production (NCP) and new production. Seasonal NCP and new production were found to be 4.57 ± 0.85 mol C m?2 and 0.37 ± 0.14 mol N m?2, respectively. The C : N ratio was high (12) compared to the Redfield ratio (6.6), and the production calculated from carbon was higher than production calculated from nitrogen, which is indicative of carbon overconsumption. The export ratio and transfer efficiency were 16 and 4 %, respectively, and the site thereby showed high flux attenuation. Particle tracking was used to examine the source region of material in the sediment trap, and there was large variation in source regions, both between and within years. There were higher correlations between surface productivity and export flux when using the particle-tracking approach, than by comparing with the mean productivity in a 100 km box around the PAP site. However, the differences in correlation coefficients were not significant, and a longer time series is needed to draw conclusions on applying particle tracking in sediment trap analyses

Syndrome dimensions and the Child Behavior Checklist and the Teacher Report Form: A critical empirical evaluation

The construct representation of the cross-informant model of the Child Behavior Checklist (CBCL) and the Teacher Report Form (TRF) was evaluated using confirmatory factor analysis. Samples were collected in seven different countries. The results are based on 13,226 parent ratings and 8893 teacher ratings. The adequacy of fit for the cross-informant model was established on the basis of three approaches: conventional rules of fit, simulation, and comparison with other models. The results indicated that the cross-informant model fits these data poorly. These results were consistent across countries, informants, and both clinical and population samples. Since inadequate empirical support for the cross-informant syndromes and their differentiation was found, the construct validity of these syndrome dimensions is questioned

Competing orders in a magnetic field: spin and charge order in the cuprate superconductors

We describe two-dimensional quantum spin fluctuations in a superconducting Abrikosov flux lattice induced by a magnetic field applied to a doped Mott insulator. Complete numerical solutions of a self-consistent large N theory provide detailed information on the phase diagram and on the spatial structure of the dynamic spin spectrum. Our results apply to phases with and without long-range spin density wave order and to the magnetic quantum critical point separating these phases. We discuss the relationship of our results to a number of recent neutron scattering measurements on the cuprate superconductors in the presence of an applied field. We compute the pinning of static charge order by the vortex cores in the `spin gap' phase where the spin order remains dynamically fluctuating, and argue that these results apply to recent scanning tunnelling microscopy (STM) measurements. We show that with a single typical set of values for the coupling constants, our model describes the field dependence of the elastic neutron scattering intensities, the absence of satellite Bragg peaks associated with the vortex lattice in existing neutron scattering observations, and the spatial extent of charge order in STM observations. We mention implications of our theory for NMR experiments. We also present a theoretical discussion of more exotic states that can be built out of the spin and charge order parameters, including spin nematics and phases with `exciton fractionalization'.Comment: 36 pages, 33 figures; for a popular introduction, see http://onsager.physics.yale.edu/superflow.html; (v2) Added reference to new work of Chen and Ting; (v3) reorganized presentation for improved clarity, and added new appendix on microscopic origin; (v4) final published version with minor change

The Impact of Perceived Expectations and Uncertainty on Firm Investment

This paper analyses the (differential) impact of perceived expectations and uncertainty on investment spending in small and large firms. We analyse two types of investment, viz. aggregate investment and investment in energy-saving technologies, using Dutch firm level data. The results show that expectations and uncertainty about input- and output prices and domestic demand have substantial but different effects on investment spending in firms of different sizes. Furthermore, we find evidence, at least for small firms, that there are important differences between the effects of uncertainty about input and output variable

Animal models for COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the aetiological agent of coronavirus disease 2019 (COVID-19), an emerging respiratory infection caused by the introduction of a novel coronavirus into humans late in 2019 (first detected in Hubei province, China). As of 18 September 2020, SARS-CoV-2 has spread to 215 countries, has infected more than 30 million people and has caused more than 950,000 deaths. As humans do not have pre-existing immunity to SARS-CoV-2, there is an urgent need to develop therapeutic agents and vaccines to mitigate the current pandemic and to prevent the re-emergence of COVID-19. In February 2020, the World Health Organization (WHO) assembled an international panel to develop animal models for COVID-19 to accelerate the testing of vaccines and therapeutic agents. Here we summarize the findings to date and provides relevant information for preclinical testing of vaccine candidates and therapeutic agents for COVID-19

Two Types of Planning in Neighborhoods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68338/2/10.1177_0739456X8400300209.pd

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting