The major burden of knee joint osteoarthritis (OA) is pain. Since in elder patients diabetes mellitus is an important comorbidity of OA, we explored whether the presence of diabetes mellitus has a significant influence on pain intensity at the end stage of knee OA, and we aimed to identify factors possibly related to changes of pain intensity in diabetic patients. In 23 diabetic and 47 nondiabetic patients with OA undergoing total knee arthroplasty, we assessed the pain intensity before the operation using the "Knee Injury and Osteoarthritis Outcome Score". Furthermore, synovial tissue, synovial fluid (SF), cartilage, and blood were obtained. We determined the synovitis score, the concentrations of prostaglandin E2 and interleukin-6 (IL-6) in the SF and serum, and of C-reactive protein and HbA1c and other metabolic parameters in the serum. We performed multivariate regression analyses to study the association of pain with several parameters. Diabetic patients had on average a higher Knee Injury and Osteoarthritis Outcome Score pain score than nondiabetic patients (P < 0.001). Knee joints from diabetic patients exhibited on average higher synovitis scores (P = 0.024) and higher concentrations of IL-6 in the SF (P = 0.003) than knee joints from nondiabetic patients. Multivariate regression analysis showed that patients with higher synovitis scores had more intense pain independent of all investigated confounders, and that the positive association between pain intensities and IL-6 levels was dependent on diabetes mellitus and/or synovitis. These data suggest that diabetes mellitus significantly increases pain intensity of knee OA, and that in diabetic patients higher pain intensities were determined by stronger synovitis.

Abstract. Ice-nucleating particles (INPs) trigger the formation of cloud ice crystals in the atmosphere. Therefore, they strongly influence cloud microphysical and optical properties and precipitation and the life cycle of clouds. Improving weather forecasting and climate projection requires an appropriate formulation of atmospheric INP concentrations. This remains challenging as the global INP distribution and variability depend on a variety of aerosol types and sources, and neither their short-term variability nor their long-term seasonal cycles are well covered by continuous measurements. Here, we provide the first year-long set of observations with a pronounced INP seasonal cycle in a boreal forest environment. Besides the observed seasonal cycle in INP concentrations with a minimum in wintertime and maxima in early and late summer, we also provide indications for a seasonal variation in the prevalent INP type. We show that the seasonal dependency of INP concentrations and prevalent INP types is most likely driven by the abundance of biogenic aerosol. As current parameterizations do not reproduce this variability, we suggest a new mechanistic description for boreal forest environments which considers the seasonal variation in INP concentrations. For this, we use the ambient air temperature measured close to the ground at 4.2 m height as a proxy for the season, which appears to affect the source strength of biogenic emissions and, thus, the INP abundance over the boreal forest. Furthermore, we provide new INP parameterizations based on the Ice Nucleation Active Surface Site (INAS) approach, which specifically describes the ice nucleation activity of boreal aerosols particles prevalent in different seasons. Our results characterize the boreal forest as an important but variable INP source and provide new perspectives to describe these new findings in atmospheric models.

Abstract. Ice-nucleating particles (INPs) trigger the formation of cloud ice crystals in the atmosphere. Therefore, they strongly influence cloud microphysical and optical properties, as well as precipitation and the life cycle of clouds. Improving weather forecasting and climate projection requires an appropriate formulation of atmospheric INP concentrations. This remains challenging, as the global INP distribution and variability depend on a variety of aerosol types and sources, and neither their short-term variability nor their long-term seasonal cycles are well covered by continuous measurements. Here, we provide the first year-long set of observations with a pronounced INP seasonal cycle in a boreal forest environment. Besides the observed seasonal cycle in INP concentrations with a minimum in wintertime and maxima in early and late summer, we also provide indications for a seasonal variation in the prevalent INP type. We show that the seasonal dependency of INP concentrations and prevalent INP types is most likely driven by the abundance of biogenic aerosol. As current parameterizations do not reproduce this variability, we suggest a new parameterization approach, which considers the seasonal variation of INP concentrations. For this, we use the ambient air temperature as a proxy for the season which affects the source strength of biogenic emissions and by that the INP abundance over the boreal forest areas. Furthermore, we provide new INP parameterizations based on the Ice Nucleation Active Surface Site (INAS) approach, which specifically describes the ice nucleation activity of boreal aerosols particles prevalent in different seasons. Our results characterize the boreal forest as an important but variable INP source and provide new perspectives to describe these new findings in atmospheric models.

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation—either by governments or social media companies—can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies—empathy, warning of consequences, and humor—or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions.