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Abstract Macrophages are the primary lung phagocyte and are instrumental in maintenance of a sterile, noninflamed microenvironment. IFNs are produced in response to bacterial and viral infection, and activate the macrophage to efficiently counteract and remove pathogenic invaders. Respiratory syncytial virus (RSV) inhibits IFN-mediated signaling mechanisms in epithelial cells; however, the effects on IFN signaling in the macrophage are currently unknown. We investigated the effect of RSV infection on IFN-mediated signaling in macrophages. RSV infection inhibited IFN-β– and IFN-γ–activated transcriptional mechanisms in primary alveolar macrophages and macrophage cell lines, including the transactivation of important Nod-like receptor family genes, Nod1 and class II transactivator. RSV inhibited IFN-β– and IFN-γ–mediated transcriptional activation by two distinct mechanisms. RSV impaired IFN-β–mediated signal transducer and activator of transcription (STAT)-1 phosphorylation through a mechanism that involves inhibition of tyrosine kinase 2 phosphorylation. In contrast, RSV-impaired transcriptional activation after IFN-γ stimulation resulted from a reduction in the nuclear STAT1 interaction with the transcriptional coactivator, CBP, and was correlated with increased phosphorylation of STAT1β, a dominant-negative STAT1 splice variant, in response to IFN-γ. In support of this concept, overexpression of STAT1β was sufficient to repress the IFN-γ–mediated expression of class II transactivator. These results demonstrate that RSV inhibits IFN-mediated transcriptional activation in macrophages, and suggests that paramyxoviruses modulate an important regulatory mechanism that is critical in linking innate and adaptive immune mechanisms after infection.

Macrophages are the primary lung phagocyte and are instrumental in maintenance of a sterile, noninflamed microenvironment. IFNs are produced in response to bacterial and viral infection, and activate the macrophage to efficiently counteract and remove pathogenic invaders. Respiratory syncytial virus (RSV) inhibits IFN-mediated signaling mechanisms in epithelial cells; however, the effects on IFN signaling in the macrophage are currently unknown. We investigated the effect of RSV infection on IFN-mediated signaling in macrophages. RSV infection inhibited IFN-beta- and IFN-gamma-activated transcriptional mechanisms in primary alveolar macrophages and macrophage cell lines, including the transactivation of important Nod-like receptor family genes, Nod1 and class II transactivator. RSV inhibited IFN-beta- and IFN-gamma-mediated transcriptional activation by two distinct mechanisms. RSV impaired IFN-beta-mediated signal transducer and activator of transcription (STAT)-1 phosphorylation through a mechanism that involves inhibition of tyrosine kinase 2 phosphorylation. In contrast, RSV-impaired transcriptional activation after IFN-gamma stimulation resulted from a reduction in the nuclear STAT1 interaction with the transcriptional coactivator, CBP, and was correlated with increased phosphorylation of STAT1beta, a dominant-negative STAT1 splice variant, in response to IFN-gamma. In support of this concept, overexpression of STAT1beta was sufficient to repress the IFN-gamma-mediated expression of class II transactivator. These results demonstrate that RSV inhibits IFN-mediated transcriptional activation in macrophages, and suggests that paramyxoviruses modulate an important regulatory mechanism that is critical in linking innate and adaptive immune mechanisms after infection.

Many patients with social anxiety disorder (SAD) experience inadequate symptom relief from available treatments. Ketamine is a potent N-methyl-D-aspartate receptor antagonist with a potentially novel mechanism of action for the treatment of anxiety disorders. Therefore, we conducted a double-blind, randomized, placebo-controlled crossover trial in 18 adults with DSM-5 SAD and compared the effects between intravenous ketamine (0.5 mg/kg over 40 min) and placebo (normal saline) on social phobia symptoms. Ketamine and placebo infusions were administered in a random order with a 28-day washout period between infusions. Ratings of anxiety were assessed 3-h post-infusion and followed for 14 days. We used linear mixed models to assess the impact of ketamine and placebo on anxiety symptoms. Outcomes were blinded ratings on the Liebowitz Social Anxiety Scale (LSAS) and self-reported anxiety on a visual analog scale (VAS-Anxiety). We also used the Wilcoxon signed-rank test to compare the proportion of treatment responders. Based on prior studies, we defined response as a greater than 35% LSAS reduction and 50% VAS-Anxiety reduction. We found ketamine resulted in a significantly greater reduction in anxiety relative to placebo on the LSAS (Time × Treatment: F9,115 =2.6, p=0.01) but not the VAS-Anxiety (Time × Treatment: F10,141 =0.4, p=0.95). Participants were significantly more likely to exhibit a treatment response after ketamine infusion relative to placebo in the first 2 weeks following infusion measured on the LSAS (33.33% response ketamine vs 0% response placebo, Wilcoxon signed-rank test z=2.24, p=0.025) and VAS (88.89% response ketamine vs 52.94% response placebo, Wilcoxon signed-rank test z=2.12, p=0.034). In conclusion, this proof-of-concept trial provides initial evidence that ketamine may be effective in reducing anxiety.

Depression is a common, debilitating, and costly disorder. Many patients request psychological therapy, but the best-evidenced therapy—cognitive behavioural therapy (CBT)—is complex and costly. A simpler therapy—behavioural activation (BA)—might be as effective and cheaper than is CBT. We aimed to establish the clinical efficacy and cost-effectiveness of BA compared with CBT for adults with depression. In this randomised, controlled, non-inferiority trial, we recruited adults aged 18 years or older meeting Diagnostic and Statistical Manual of Mental Disorders IV criteria for major depressive disorder from primary care and psychological therapy services in Devon, Durham, and Leeds (UK). We excluded people who were receiving psychological therapy, were alcohol or drug dependent, were acutely suicidal or had attempted suicide in the previous 2 months, or were cognitively impaired, or who had bipolar disorder or psychosis or psychotic symptoms. We randomly assigned participants (1:1) remotely using computer-generated allocation (minimisation used; stratified by depression severity [Patient Health Questionnaire 9 (PHQ-9) score of <19 vs ≥19], antidepressant use, and recruitment site) to BA from junior mental health workers or CBT from psychological therapists. Randomisation done at the Peninsula Clinical Trials Unit was concealed from investigators. Treatment was given open label, but outcome assessors were masked. The primary outcome was depression symptoms according to the PHQ-9 at 12 months. We analysed all those who were randomly allocated and had complete data (modified intention to treat [mITT]) and also all those who were randomly allocated, had complete data, and received at least eight treatment sessions (per protocol [PP]). We analysed safety in the mITT population. The non-inferiority margin was 1·9 PHQ-9 points. This trial is registered with the ISCRTN registry, number ISRCTN27473954. Between Sept 26, 2012, and April 3, 2014, we randomly allocated 221 (50%) participants to BA and 219 (50%) to CBT. 175 (79%) participants were assessable for the primary outcome in the mITT population in the BA group compared with 189 (86%) in the CBT group, whereas 135 (61%) were assessable in the PP population in the BA group compared with 151 (69%) in the CBT group. BA was non-inferior to CBT (mITT: CBT 8·4 PHQ-9 points [SD 7·5], BA 8·4 PHQ-9 points [7·0], mean difference 0·1 PHQ-9 points [95% CI −1·3 to 1·5], p=0·89; PP: CBT 7·9 PHQ-9 points [7·3]; BA 7·8 [6·5], mean difference 0·0 PHQ-9 points [–1·5 to 1·6], p=0·99). Two (1%) non-trial-related deaths (one [1%] multidrug toxicity in the BA group and one [1%] cancer in the CBT group) and 15 depression-related, but not treatment-related, serious adverse events (three in the BA group and 12 in the CBT group) occurred in three [2%] participants in the BA group (two [1%] patients who overdosed and one [1%] who self-harmed) and eight (4%) participants in the CBT group (seven [4%] who overdosed and one [1%] who self-harmed). We found that BA, a simpler psychological treatment than CBT, can be delivered by junior mental health workers with less intensive and costly training, with no lesser effect than CBT. Effective psychological therapy for depression can be delivered without the need for costly and highly trained professionals. National Institute for Health Research.

Paranoia is the belief that harm is intended by others. It may arise from selective pressures to infer and avoid social threats, particularly in ambiguous or changing circumstances. We propose that uncertainty may be sufficient to elicit learning differences in paranoid individuals, without social threat. We used reversal learning behavior and computational modeling to estimate belief updating across individuals with and without mental illness, online participants, and rats chronically exposed to methamphetamine, an elicitor of paranoia in humans. Paranoia is associated with a stronger prior on volatility, accompanied by elevated sensitivity to perceived changes in the task environment. Methamphetamine exposure in rats recapitulates this impaired uncertainty-driven belief updating and rigid anticipation of a volatile environment. Our work provides evidence of fundamental, domain-general learning differences in paranoid individuals. This paradigm enables further assessment of the interplay between uncertainty and belief-updating across individuals and species. Everyone has had fleeting concerns that others might be against them at some point in their lives. Sometimes these concerns can escalate into paranoia and become debilitating. Paranoia is a common symptom in serious mental illnesses like schizophrenia. It can cause extreme distress and is linked with an increased risk of violence towards oneself or others. Understanding what happens in the brains of people experiencing paranoia might lead to better ways to treat or manage it. Some experts argue that paranoia is caused by errors in the way people assess social situations. An alternative idea is that paranoia stems from the way the brain forms and updates beliefs about the world. Now, Reed et al. show that both people with paranoia and rats exposed to a paranoia-inducing substance expect the world will change frequently, change their minds often, and have a harder time learning in response to changing circumstances. In the experiments, human volunteers with and without psychiatric disorders played a game where the best choices change. Then, the participants completed a survey to assess their level of paranoia. People with higher levels of paranoia predicted more changes would occur and made less predictable choices. In a second set of experiments, rats were put in a cage with three holes where they sometimes received sugar rewards. Some of the rats received methamphetamine, a drug that causes paranoia in humans. Rats given the drug also expected the location of the sugar reward would change often. The drugged animals had harder time learning and adapting to changing circumstances. The experiments suggest that brain processes found in both rats, which are less social than humans, and humans contribute to paranoia. This suggests paranoia may make it harder to update beliefs. This may help scientists understand what causes paranoia and develop therapies or drugs that can reduce paranoia. This information may also help scientists understand why during societal crises like wars or natural disasters humans are prone to believing conspiracies. This is particularly important now as the world grapples with climate change and a global pandemic. Reed et al. note paranoia may impede the coordination of collaborative solutions to these challenging situations.

It is the purpose of this paper to provide a comprehensive documentation of the new NCAR (National Center for Atmospheric Research) version of the spectral element (SE) dynamical core as part of the Community Earth System Model (CESM2.0) release. This version differs from previous releases of the SE dynamical core in several ways. Most notably the hybrid sigma vertical coordinate is based on dry air mass, the condensates are dynamically active in the thermodynamic and momentum equations (also referred to as condensate loading), and the continuous equations of motion conserve a more comprehensive total energy that includes condensates. Not related to the vertical coordinate change, the hyperviscosity operators and the vertical remapping algorithms have been modified. The code base has been significantly reduced, sped up, and cleaned up as part of integrating SE as a dynamical core in the CAM (Community Atmosphere Model) repository rather than importing the SE dynamical core from High‐Order Methods Modeling environment as an external code. Key Points The CESM2.0 release of the spectral element dynamical core (CAM‐SE) is documented Model has comprehensive treatment of condensates and energy The CAM‐SE model has been sped up significantly compared to its predecessor CAM‐HOMME

There is a growing understanding of the role that bedrock weathering can play as a source of nitrogen (N) to soils, groundwater and river systems. The significance is particularly apparent in mountainous environments where weathering fluxes can be large. However, our understanding of the relative contributions of rock-derived, or geogenic, N to the total N supply of mountainous watersheds remains poorly understood. In this study, we develop the High-Altitude Nitrogen Suite of Models (HAN-SoMo), a watershed-scale ensemble of process-based models to quantify the relative sources, transformations, and sinks of geogenic and atmospheric N through a mountain watershed. Our study is based in the East River Watershed (ERW) in the Upper Colorado River Basin. The East River is a near-pristine headwater watershed underlain primarily by an N-rich Mancos Shale bedrock, enabling the timing and magnitude of geogenic and atmospheric contributions to watershed scale dissolved N-exports to be quantified. Several calibration scenarios were developed to explore equifinality using >1600 N concentration measurements from streams, groundwater, and vadose zone samples collected over the course of four years across the watershed. When accounting for recycling of N through plant litter turnover, rock weathering accounts for approximately 12% of the annual dissolved N sources to the watershed in the most probable calibration scenario (0–31% in other scenarios), and 21% (0–44% in other scenarios) when considering only “new” N sources (i.e. geogenic and atmospheric). On an annual scale, instream dissolved N elimination, plant turnover (including cattle grazing) and atmospheric deposition are the most important controls on N cycling.

Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth system above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths, and limitations, so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities, and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when, or where large-scale models achieve their purpose or where opportunities for improvements exist so that such models better achieve their purpose. We suggest that reproducing the spatiotemporal details of regional-scale models and matching local data are not relevant goals. Instead, it is important to decide on reasonable model expectations regarding when a large-scale model is performing “well enough” in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria are quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We describe current modeling strategies and evaluation practices, and we subsequently discuss the value of three evaluation strategies: (1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation), (2) comparing several models with each other with or without reference to actual observations (model-based evaluation), and (3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning, and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models, thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

This paper describes and analyzes the Reed–Jablonowski (RJ) tropical cyclone (TC) test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). This intermediate-complexity test case analyzes the evolution of a weak vortex into a TC in an idealized tropical environment. Reference solutions from nine general circulation models (GCMs) with identical simplified physics parameterization packages that participated in DCMIP2016 are analyzed in this study at 50 km horizontal grid spacing, with five of these models also providing solutions at 25 km grid spacing. Evolution of minimum surface pressure (MSP) and maximum 1 km azimuthally averaged wind speed (MWS), the wind–pressure relationship, radial profiles of wind speed and surface pressure, and wind composites are presented for all participating GCMs at both horizontal grid spacings. While all TCs undergo a similar evolution process, some reach significantly higher intensities than others, ultimately impacting their horizontal and vertical structures. TCs simulated at 25 km grid spacings retain these differences but reach higher intensities and are more compact than their 50 km counterparts. These results indicate that dynamical core choice is an essential factor in GCM development, and future work should be conducted to explore how specific differences within the dynamical core affect TC behavior in GCMs.