Abstract: Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Abstract: Abstract. This paper presents extensive {bias determination} analyses of ozone observations from the Atmospheric Chemistry Experiment (ACE) satellite instruments: the ACE Fourier Transform Spectrometer (ACE-FTS) and the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (ACE-MAESTRO) instrument. Here we compare the latest ozone data products from ACE-FTS and ACE-MAESTRO with coincident observations from nearly 20 satellite-borne, airborne, balloon-borne and ground-based instruments, by analysing volume mixing ratio profiles and partial column densities. The ACE-FTS version 2.2 Ozone Update product reports more ozone than most correlative measurements from the upper troposphere to the lower mesosphere. At altitude levels from 16 to 44 km, the average values of the mean relative differences are nearly all within +1 to +8%. At higher altitudes (45–60 km), the ACE-FTS ozone amounts are significantly larger than those of the comparison instruments, with mean relative differences of up to +40% (about +20% on average). For the ACE-MAESTRO version 1.2 ozone data product, mean relative differences are within ±10% (average values within ±6%) between 18 and 40 km for both the sunrise and sunset measurements. At higher altitudes (~35–55 km), systematic biases of opposite sign are found between the ACE-MAESTRO sunrise and sunset observations. While ozone amounts derived from the ACE-MAESTRO sunrise occultation data are often smaller than the coincident observations (with mean relative differences down to −10%), the sunset occultation profiles for ACE-MAESTRO show results that are qualitatively similar to ACE-FTS, indicating a large positive bias (mean relative differences within +10 to +30%) in the 45–55 km altitude range. In contrast, there is no significant systematic difference in bias found for the ACE-FTS sunrise and sunset measurements.

Abstract: Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC) 3 project was established in 2016 ( www.ac3-tr.de/ ). It comprises modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, shipborne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data. For example, a distinct atmospheric moistening, an increase of regional storm activities, an amplified winter warming in the Svalbard and North Pole regions, and a decrease of sea ice thickness in the Fram Strait and of snow depth on sea ice have been identified. A positive trend of tropospheric bromine monoxide (BrO) column densities during polar spring was verified. Local marine/biogenic sources for cloud condensation nuclei and ice nucleating particles were found. Atmospheric–ocean and radiative transfer models were advanced by applying new parameterizations of surface albedo, cloud droplet activation, convective plumes and related processes over leads, and turbulent transfer coefficients for stable surface layers. Four modes of the surface radiative energy budget were explored and reproduced by simulations. To advance the future synthesis of the results, cross-cutting activities are being developed aiming to answer key questions in four focus areas: lapse rate feedback, surface processes, Arctic mixed-phase clouds, and airmass transport and transformation.

Abstract: Abstract. We discuss the quality of the two available SCIAMACHY limb ozone profile products. They were retrieved with the University of Bremen IFE's algorithm version 1.61 (hereafter IFE), and the official ESA offline algorithm (hereafter OL) versions 2.4 and 2.5. The ozone profiles were compared to a suite of correlative measurements from ground-based lidar and microwave, sondes, SAGE II and SAGE III (Stratospheric Aerosol and Gas Experiment). To correct for the expected Envisat pointing errors, which have not been corrected implicitly in either of the algorithms, we applied a constant altitude shift of -1.5 km to the SCIAMACHY ozone profiles. The IFE ozone profile data between 16 and 40 km are biased low by 3-6%. The average difference profiles have a typical standard deviation of 10% between 20 and 35 km. We show that more than 20% of the SCIAMACHY official ESA offline (OL) ozone profiles version 2.4 and 2.5 have unrealistic ozone values, most of these are north of 15° S. The remaining OL profiles compare well to correlative instruments above 24 km. Between 20 and 24 km, they underestimate ozone by 15±5%.

Abstract: Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), on-board the European ENVIronmental SATellite (ENVISAT) launched on 1 March 2002, is a middle infrared Fourier Transform spectrometer measuring the atmospheric emission spectrum in limb sounding geometry. The instrument is capable to retrieve the vertical distribution of temperature and trace gases, aiming at the study of climate and atmospheric chemistry and dynamics, and at applications to data assimilation and weather forecasting. MIPAS operated in its standard observation mode for approximately two years, from July 2002 to March 2004, with scans performed at nominal spectral resolution of 0.025 cm−1 and covering the altitude range from the mesosphere to the upper troposphere with relatively high vertical resolution (about 3 km in the stratosphere). Only reduced spectral resolution measurements have been performed subsequently. MIPAS data were re-processed by ESA using updated versions of the Instrument Processing Facility (IPF v4.61 and v4.62) and provided a complete set of level-2 operational products (geo-located vertical profiles of temperature and volume mixing ratio of H2O, O3, HNO3, CH4, N2O and NO2) with quasi continuous and global coverage in the period of MIPAS full spectral resolution mission. In this paper, we report a detailed description of the validation of MIPAS-ENVISAT operational ozone data, that was based on the comparison between MIPAS v4.61 (and, to a lesser extent, v4.62) O3 VMR profiles and a comprehensive set of correlative data, including observations from ozone sondes, ground-based lidar, FTIR and microwave radiometers, remote-sensing and in situ instruments on-board stratospheric aircraft and balloons, concurrent satellite sensors and ozone fields assimilated by the European Center for Medium-range Weather Forecasting. A coordinated effort was carried out, using common criteria for the selection of individual validation data sets, and similar methods for the comparisons. This enabled merging the individual results from a variety of independent reference measurements of proven quality (i.e. well characterized error budget) into an overall evaluation of MIPAS O3 data quality, having both statistical strength and the widest spatial and temporal coverage. Collocated measurements from ozone sondes and ground-based lidar and microwave radiometers of the Network for the Detection Atmospheric Composition Change (NDACC) were selected to carry out comparisons with time series of MIPAS O3 partial columns and to identify groups of stations and time periods with a uniform pattern of ozone differences, that were subsequently used for a vertically resolved statistical analysis. The results of the comparison are classified according to synoptic and regional systems and to altitude intervals, showing a generally good agreement within the comparison error bars in the upper and middle stratosphere. Significant differences emerge in the lower stratosphere and are only partly explained by the larger contributions of horizontal and vertical smoothing differences and of collocation errors to the total uncertainty. Further results obtained from a purely statistical analysis of the same data set from NDACC ground-based lidar stations, as well as from additional ozone soundings at middle latitudes and from NDACC ground-based FTIR measurements, confirm the validity of MIPAS O3 profiles down to the lower stratosphere, with evidence of larger discrepancies at the lowest altitudes. The validation against O3 VMR profiles using collocated observations performed by other satellite sensors (SAGE II, POAM III, ODIN-SMR, ACE-FTS, HALOE, GOME) and ECMWF assimilated ozone fields leads to consistent results, that are to a great extent compatible with those obtained from the comparison with ground-based measurements. Excellent agreement in the full vertical range of the comparison is shown with respect to collocated ozone data from stratospheric aircraft and balloon instruments, that was mostly obtained in very good spatial and temporal coincidence with MIPAS scans. This might suggest that the larger differences observed in the upper troposphere and lowermost stratosphere with respect to collocated ground-based and satellite O3 data are only partly due to a degradation of MIPAS data quality. They should be rather largely ascribed to the natural variability of these altitude regions and to other components of the comparison errors. By combining the results of this large number of validation data sets we derived a general assessment of MIPAS v4.61 and v4.62 ozone data quality. A clear indication of the validity of MIPAS O3 vertical profiles is obtained for most of the stratosphere, where the mean relative difference with the individual correlative data sets is always lower than ±10%. Furthermore, these differences always fall within the combined systematic error (from 1 hPa to 50 hPa) and the standard deviation is fully consistent with the random error of the comparison (from 1 hPa to ~30–40 hPa). A degradation in the quality of the agreement is generally observed in the lower stratosphere and upper troposphere, with biases up to 25% at 100 hPa and standard deviation of the global mean differences up to three times larger than the combined random error in the range 50–100 hPa. The larger differences observed at the bottom end of MIPAS retrieved profiles can be associated, as already noticed, to the effects of stronger atmospheric gradients in the UTLS that are perceived differently by the various measurement techniques. However, further components that may degrade the results of the comparison at lower altitudes can be identified as potentially including cloud contamination, which is likely not to have been fully filtered using the current settings of the MIPAS cloud detection algorithm, and in the linear approximation of the forward model that was used for the a priori estimate of systematic error components. The latter, when affecting systematic contributions with a random variability over the spatial and temporal scales of global averages, might result in an underestimation of the random error of the comparison and add up to other error sources, such as the possible underestimates of the p and T error propagation based on the assumption of a 1 K and 2% uncertainties, respectively, on MIPAS temperature and pressure retrievals. At pressure lower than 1 hPa, only a small fraction of the selected validation data set provides correlative ozone data of adequate quality and it is difficult to derive quantitative conclusions about the performance of MIPAS O3 retrieval for the topmost layers.

Abstract: Abs have a paramount place in the treatment of certain, mainly lymphoid, malignancies, although tumors of nonhemopoietic origin have proved more refractory ones. We have previously shown that the efficacy of immunotherapy of solid tumors, in particular ovarian carcinoma, may be improved by the use of IgE Abs in place of the conventional IgG. An IgE Ab (MOv18 IgE) against an ovarian-tumor-specific Ag (folate binding protein), in combination with human PBMC, introduced into ovarian cancer xenograft-bearing mice, greatly exceeded the analogous IgG1 in promoting survival. In this study, we analyzed the mechanisms by which MOv18 IgE may exert its antitumor activities. Monocytes were essential IgE receptor-expressing effector cells that mediated the enhanced survival of tumor-bearing mice by MOv18 IgE and human PBMC. Monocytes mediated MOv18 IgE-dependent ovarian tumor cell killing in vitro by two distinct pathways, cytotoxicity and phagocytosis, acting respectively through the IgE receptors FcεRI and CD23. We also show that human eosinophils were potent effector cells in MOv18 IgE Ab-dependent ovarian tumor cell cytotoxicity in vitro. These results demonstrate that IgE Abs can engage cell surface IgE receptors and activate effector cells against ovarian tumor cells. Our findings offer a framework for an improved immunotherapeutic strategy for combating solid tumors.

Abstract: IgE antibodies are key mediators of antiparasitic immune responses, but their potential for cancer treatment via antibody-dependent cell-mediated cytotoxicity (ADCC) has been little studied. Recently, tumor antigen–specific IgEs were reported to restrict cancer cell growth by engaging high-affinity Fc receptors on monocytes and macrophages; however, the underlying therapeutic mechanisms were undefined and in vivo proof of concept was limited. Here, an immunocompetent rat model was designed to recapitulate the human IgE-Fcϵ receptor system for cancer studies. We also generated rat IgE and IgG mAbs specific for the folate receptor (FRα), which is expressed widely on human ovarian tumors, along with a syngeneic rat tumor model expressing human FRα. Compared with IgG, anti-FRα IgE reduced lung metastases. This effect was associated with increased intratumoral infiltration by TNFα+ and CD80+ macrophages plus elevated TNFα and the macrophage chemoattractant MCP-1 in lung bronchoalveolar lavage fluid. Increased levels of TNFα and MCP-1 correlated with IgE-mediated tumor cytotoxicity by human monocytes and with longer patient survival in clinical specimens of ovarian cancer. Monocytes responded to IgE but not IgG exposure by upregulating TNFα, which in turn induced MCP-1 production by monocytes and tumor cells to promote a monocyte chemotactic response. Conversely, blocking TNFα receptor signaling abrogated induction of MCP-1, implicating it in the antitumor effects of IgE. Overall, these findings show how antitumor IgE reprograms monocytes and macrophages in the tumor microenvironment, encouraging the clinical use of IgE antibody technology to attack cancer beyond the present exclusive reliance on IgG. Cancer Res; 77(5); 1127–41. ©2017 AACR.

Abstract: Research has shown differences in subcortical brain volumes between participants with schizophrenia and healthy controls. However, none of these differences have been found to associate with schizophrenia polygenic risk. Here, in a large sample ( n  = 14,701) of unaffected participants from the UK Biobank, we test whether schizophrenia polygenic risk scores (PRS) limited to specific gene-sets predict subcortical brain volumes. We compare associations with schizophrenia PRS at the whole genome level (‘genomic’, including all SNPs associated with the disorder at a p -value threshold 〈  0.05) with ‘genic’ PRS (based on SNPs in the vicinity of known genes), ‘intergenic’ PRS (based on the remaining SNPs), and genic PRS limited to SNPs within 7 gene-sets previously found to be enriched for genetic association with schizophrenia (‘abnormal behaviour,’ ‘abnormal long-term potentiation,’ ‘abnormal nervous system electrophysiology,’ ‘FMRP targets,’ ‘5HT2C channels,’ ‘CaV2 channels’ and ‘loss-of-function intolerant genes’). We observe a negative association between the ‘abnormal behaviour’ gene-set PRS and volume of the right thalamus that survived correction for multiple testing (ß = −0.031, p FDR  = 0.005) and was robust to different schizophrenia PRS p -value thresholds. In contrast, the only association with genomic PRS surviving correction for multiple testing was for right pallidum, which was observed using a schizophrenia PRS p -value threshold 〈  0.01 (ß = −0.032, p  = 0.0003, p FDR  = 0.02), but not when using other PRS P -value thresholds. We conclude that schizophrenia PRS limited to functional gene sets may provide a better means of capturing differences in subcortical brain volume than whole genome PRS approaches.

Abstract: Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.