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Vector embedding spaces are representational structures that can capture both the similarity relationship between items and various other semantic relationships. Current state-of-the-art embedding models can generate embedding vectors for individual words and longer strings of text, enabling vector spaces to encode the similarity between entire documents of text. We investigated three embedding models to see if semantic relationships besides similarity are represented in these spaces across three embedding models, focusing on the relationship between arguments and counterarguments as a specific example. While there was not a linear subspace that captured the semantic relationship between an argument and its counterargument, we found that neural networks with a single hidden layer could partially learn the transformations between an argument's embedding and the corresponding counterargument's embedding in all three spaces. The trained models generalized across three different datasets of arguments, suggesting these partially learned transformations are applicable to arguments and counterarguments in general, not just tied to the semantic context of the models' training dataset. This approach has practical applications in designing information retrieval systems for intelligent agents and, potentially, in models of cognition that use vector embedding spaces as a representational structure.

Abstract In this letter we use a perturbative approach to find the spectrum of non-relativistic strings in the String Newton-Cartan (SNC) AdS5×S5 spacetime. We perturb the bosonic sector of the action around a BMN-like folded string solution in light-cone gauge. We find strong evidence that the theory is described by a combination of massive and massless free fields in an anti-de Sitter background by showing that interaction terms up to six scalars vanish after field redefinitions.

Self-regulated learning (SRL) is crucial to students' learning. SRL is characterized by students taking initiative, showing perseverance and adaptively regulating their learning. Teachers play an essential role in promoting and fostering this process. However, several studies have shown that in primary education explicit instruction of SRL strategies barely takes place. Given the relevance of SRL for learning and preparing students for the knowledge society of the 21st century, it is of crucial importance that teachers in primary education learn how they can improve their students' SRL. In the present study, we implemented a professional development program (iSELF) in which primary teachers were trained and coached in promoting and fostering their students' SRL. The extent to which iSELF contributed to teachers' explicit instruction of SRL strategies was evaluated in a quasi-experimental pre-test-post-test design using video-based classroom observations. Thirty teachers from fourteen different primary schools participated in this study and were assigned to either a control (twelve teachers) or an experimental group (eighteen teachers). Results indicate that in both conditions explicit SRL strategy instruction is rare. However, explicit instruction of SRL strategies is significantly higher in the experimental group on the post-test compared to the control group showing that teachers do benefit from learning about explicit SRL instruction.

Abstract The single scattering albedo (SSA) of aerosol particles is one of the key variables that determine aerosol radiative forcing. Herein, an Algorithm for the retrieval of Single scattering albedo over Land (ASL) is proposed for application to full-disk data from the advanced Himawari imager (AHI) sensor flying on board the Himawari-8 satellite. In this algorithm, an atmospheric radiative transfer model known as the USM (the top of the atmosphere reflectance as the sum of Un-scattered, Single-scattered, and Multiple-scattered components) is used to calculate the SSA instead of predetermining the aerosol model; the USM is constrained by the surface bidirectional reflectance distribution function shape and aerosol optical depth (AOD) in the retrieval process. Combining two consecutive observations and a 2 * 2 pixel window, the optimal estimation algorithm is adopted to obtain the optimal solution for the aerosol SSA. These SSA results are evaluated by comparing with aerosol robotic network (AERONET) data. Linear regression shows that SSAASL = 0.60*SSSAERONET + 0.38, with a correlation coefficient (0.7284), mean absolute error (0.0319), mean bias error (0.00324), root mean square error (0.0427), and ~80.11% of the ASL SSA data within an uncertainty of ±0.05 of the AERONET data. A comparison of the ASL SSA products with collocated Himawari-8 SSA products (Version 03, officially released by the Japan Meteorological Agency (JMA), referred to herein as JMA SSA) shows that the accuracy of the ASL SSA is better than that of the JMA SSA products. For the SSA retrieval in large AODs (>0.4), the validation metrics vs. AERONET data are better.

Objectives Methotrexate (MTX) is one of the most commonly used medications to treat rheumatoid arthritis (RA). However, the effect of MTX treatment on cellular immune responses remains incompletely understood. This raises concerns about the vulnerability of these patients to emerging infections and following vaccination.Methods In the current study, we investigated the impact of MTX treatment in patients with immune-mediated inflammatory disease on B and CD4 T cell SARS-CoV-2 vaccination responses. Eighteen patients with RA and two patients with psoriatic arthritis on MTX monotherapy were included, as well as 10 patients with RA without immunosuppressive treatment, and 29 healthy controls. CD4 T and B cell responses were analysed 7 days and 3–6 months after two SARS-CoV-2 messenger RNA vaccinations. High-dimensional flow cytometry analysis was used to analyse fresh whole blood, an activation-induced marker assay to measure antigen-specific CD4 T cells, and spike probes to study antigen-specific B cells.Results Seven days following two SARS-CoV-2 vaccinations, total B and T cell counts were similar between MTX-treated patients and controls. In addition, spike-specific B cell frequencies were unaffected. Remarkably, the frequency of antigen-specific CD4 T cells was reduced in patients using MTX and correlated strongly with anti-RBD IgG antibodies. These results suggest that decreased CD4 T cell activity may result in slower vaccination antibody responses in MTX-treated patients.Conclusion Taken together, MTX treatment reduces vaccine-induced CD4 T cell activation, which correlates with lower antibody responses.Trial registration number NL8900.

BackgroundDysregulated innate immune responses underlie multiple inflammatory diseases, but clinical translation of preclinical innate immunity research in mice is hampered by the difficulty of studying human inflammatory reactions in an in vivo context. We therefore sought to establish in vivo human inflammatory responses in NSG-QUAD mice that express four human myelopoiesis transgenes to improve engraftment of a human innate immune system.MethodsWe reconstituted NSG-QUAD mice with human hematopoietic stem and progenitor cells (HSPCs), after which we evaluated human myeloid cell development and subsequent human responses to systemic and local lipopolysaccharide (LPS) challenges.ResultsNSG-QUAD mice already displayed engraftment of human monocytes, dendritic cells and granulocytes in peripheral blood, spleen and liver at 6 weeks after HSPC reconstitution, in which both classical, intermediate and non-classical monocytes were present. These huNSG-QUAD mice responded to intraperitoneal and intranasal LPS challenges with production of NF-κB-dependent human cytokines, a human type I interferon response, as well as inflammasome-mediated production of human IL-1β and IL-18. The latter were specifically abrogated by the NLRP3 inhibitor MCC950, while LPS-induced human monocyte death was not altered. Besides providing proof-of-principle for small molecule testing of human inflammatory reactions in huNSG-QUAD mice, this observation suggests that LPS-induced in vivo release of human NLRP3 inflammasome-generated cytokines occurs in a cell death-independent manner.ConclusionHuNSG-QUAD mice are competent for the NF-κB, interferon and inflammasome effectors of human innate immunity, and can thus be utilized to investigate signaling mechanisms and pharmacological targeting of human inflammatory responses in an in vivo setting.

ObjectivesSARS-CoV-2 infection is associated with a decline in functional outcomes; many patients experience persistent symptoms, while the underlying pathophysiology remains unclear. This study investigated white matter (WM) integrity on brain MRI in hospitalized COVID-19 patients and its associations with clinical outcomes, including long COVID.Materials and methodsWe included hospitalized COVID-19 patients and controls from CORONavirus and Ischemic Stroke (CORONIS), an observational cohort study, who underwent MRI-DWI imaging at baseline shortly after discharge (

The relative influence of geography, currents, and environment on gene flow within sessile marine species remains an open question. Detecting subtle genetic differentiation at small scales is challenging in benthic populations due to large effective population sizes, general lack of resolution in genetic markers, and because barriers to dispersal often remain elusive. Marine lakes can circumvent confounding factors by providing discrete and replicated ecosystems. Using high-resolution double digest restriction-site-associated DNA sequencing (4826 Single Nucleotide Polymorphisms, SNPs), we genotyped populations of the sponge Suberites diversicolor (n = 125) to test the relative importance of spatial scales (1-1400 km), local environmental conditions, and permeability of seascape barriers in shaping population genomic structure. With the SNP dataset, we show strong intralineage population structure, even at scales

Society faces challenges such as caring for sustainable agriculture, clean energy and restoring biodiversity, whilst developing housing and industries. Climate change meanwhile stresses the Dutch water management system, impacts flood risk management and fresh water supply. To ensure making the right decisions, which we will not regret in 100 years, we developed the concept of flood resilient landscapes. The concept of flood resilient landscapes confronts, with a perspective of long term development, desired socio-economic developments with carrying capacity and potential of underlying physical landscape conditions. The underlying principle is to create social added value while promoting or at least maintaining flood risk management, given (future) spatial and societal developments. The first results are so promising that the Dutch Flood Protection Programme aims to incorporate it. The flood resilient landscapes concept offers the prospect of keeping the Netherlands safe beyond 2100 at socially acceptable costs and with public support now and in the future and paves the way towards implementation throughout international deltas.

The sensitivity (S) of cloud parameters to the influence of different aerosol and meteorological parameters has in most previous aerosol–cloud interaction (aci) studies been addressed using traditional statistical methods. In the current study, relationships between cloud droplet effective radius (CER) and aerosol optical depth (AOD, used as a proxy for cloud condensation nuclei, CCN), i.e., the sensitivity (S) of CER to AOD, are investigated with different constraints of AOD and cloud liquid water path (LWP). In addition to traditional statistical methods, the geographical detector method (GDM) is applied in this study to quantify the relative importance of the effects of aerosol and meteorological parameters, as well as their interaction, on S. Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 data and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis data, for the period from 2008 to 2022, were used to investigate aci over eastern China. Two contrasting areas were selected: the heavily polluted Yangtze River Delta (YRD) and a relatively clean area over the East China Sea (ECS). Linear regression analysis shows that CER decreases with the increase in AOD (negative S) in the moderately polluted atmosphere (0.10.3) over the YRD. Evaluation of S as function of the LWP shows that in the moderately polluted atmosphere over the ECS, S is negative in the LWP interval [40 g m−2, 200 g m−2], and the sensitivity of CER to AOD is substantially stronger as LWP is larger. In contrast, in the polluted atmosphere over the YRD, S is positive in the LWP interval [0 g m−2, 120 g m−2] and does not change notably as function of LWP in this interval. The study further shows that over the ECS, the CER is larger for higher low tropospheric stability (LTS) and relative humidity (RH) but lower for higher pressure vertical velocity (PVV). Over the YRD, there is no significant influence of LTS on the relationship between CER and AOD. The GDM has been used as an independent method to analyze the sensitivity of cloud parameters to AOD and meteorological parameters (RH, LTS and PVV). The GDM has also been used to analyze the effects of interactions between two parameters and thus obtain information on confounding meteorological effects on the aci. Over the ECS, cloud parameters are sensitive to almost all parameters considered except for cloud top pressure (CTP), and the sensitivity to AOD is larger than that to any of the meteorological factors. Among the meteorological factors, the cloud parameters are most sensitive to PVV and least sensitive to RH. Over the YRD, the explanatory power of the sensitivity of cloud parameters to AOD and meteorological parameters is much smaller than over the ECS, except for RH, which has a statistically significant influence on CTP and can explain 74 % of the variation of CTP. The results from the GDM analysis show that cloud parameters are more sensitive to the combination of aerosol and a meteorological parameter than to each parameter alone, but confounding effects due to co-variation of both parameters cannot be excluded.

Background One of the most robust risk factors for developing a mood disorder is having a parent with a mood disorder. Unfortunately, mechanisms explaining the transmission of mood disorders from one generation to the next remain largely elusive. Since timely intervention is associated with a better outcome and prognosis, early detection of intergenerational transmission of mood disorders is of paramount importance. Here, we describe the design of the Mood and Resilience in Offspring (MARIO) cohort study in which we investigate: 1. differences in clinical, biological and environmental (e.g., psychosocial factors, substance use or stressful life events) risk and resilience factors in children of parents with and without mood disorders, and 2. mechanisms of intergenerational transmission of mood disorders via clinical, biological and environmental risk and resilience factors. Methods MARIO is an observational, longitudinal cohort study that aims to include 450 offspring of parents with a mood disorder (uni- or bipolar mood disorders) and 100-150 offspring of parents without a mood disorder aged 10-25 years. Power analyses indicate that this sample size is sufficient to detect small to medium sized effects. Offspring are recruited via existing Dutch studies involving patients with a mood disorder and healthy controls, for which detailed clinical, environmental and biological data of the index-parent (i.e., the initially identified parent with or without a mood disorder) is available. Over a period of three years, four assessments will take place, in which extensive clinical, biological and environmental data and data on risk and resilience are collected through e.g., blood sampling, face-to-face interviews, online questionnaires, actigraphy and Experience Sampling Method assessment. For co-parents, information on demographics, mental disorder status and a DNA-sample are collected. Discussion The MARIO cohort study is a large longitudinal cohort study among offspring of parents with and without mood disorders. A unique aspect is the collection of granular data on clinical, biological and environmental risk and resilience factors in offspring, in addition to available parental data on many similar factors. We aim to investigate the mechanisms underlying intergenerational transmission of mood disorders, which will ultimately lead to better outcomes for offspring at high familial risk.