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Über kaum ein Thema werden so hitzige Debatten geführt wie über Geschlechtsidentität. Das Wissen darum, dass Gender sozial konstruiert ist, wird von Anti-Gender Aktivist*innen häufig als ‚Gender-Ideologie‘ bezeichnet und ruft heftige Gegenreaktionen hervor. Dies gilt nicht nur in Deutschland – sondern länderübergreifend. Auffällig viele der transnationalen Anti- Gender Mobilisierungen der letzten 20 Jahre finden bezogen auf Bildungseinrichtungen statt. Dieser Beitrag widmet sich der besonderen Rolle der Universität und der Wissenschaft für transnationale Anti-Gender Diskurse. Anhand verschiedener Beispiele zeige ich auf, dass das Verhältnis zwischen Anti-Gender Bewegungen und Wissenschaft geprägt ist von widersprüchlichen Dynamiken, von Abgrenzung aber auch Imitation. In ihrem Zusammenspiel wirken beide Dynamiken mobilisierend und tragen zum Erstarken regressiver Rollenbilder und antidemokratischer rechter Bewegungen in der breiteren Gesellschaft bei. Der letzte Teil des Beitrags ruft daher zu mehr Selbstreflexion der wissenschaftlichen Praxis auf Grundlage feministischer und intersektionaler Ansätze auf.
Satisfaction and frustration of the needs for autonomy, competence, and relatedness, as assessed with the 24-item Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS), have been found to be crucial indicators of individuals’ psychological health. To increase the usability of this scale within a clinical and health services research context, we aimed to validate a German short version (12 items) of this scale in individuals with depression including the examination of the relations from need frustration and need satisfaction to ill-being and quality of life (QOL). This cross-sectional study involved 344 adults diagnosed with depression (Mage (SD) = 47.5 years (11.1); 71.8% females). Confirmatory factor analyses indicated that the short version of the BPNSFS was not only reliable, but also fitted a six-factor structure (i.e., satisfaction/frustration X type of need). Subsequent structural equation modeling showed that need frustration related positively to indicators of ill-being and negatively to QOL. Surprisingly, need satisfaction did not predict differences in ill-being or QOL. The short form of the BPNSFS represents a practical instrument to measure need satisfaction and frustration in people with depression. Further, the results support recent evidence on the importance of especially need frustration in the prediction of psychopathology.
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
(2023)
Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.
Using time-resolved x-ray diffraction, we demonstrate the manipulation of the picosecond strain response of a metallic heterostructure consisting of a dysprosium (Dy) transducer and a niobium (Nb) detection layer by an external magnetic field. We utilize the first-order ferromagnetic–antiferromagnetic phase transition of the Dy layer, which provides an additional large contractive stress upon laser excitation compared to its zerofield response. This enhances the laser-induced contraction of the transducer and changes the shape of the picosecond strain pulses driven in Dy and detected within the buried Nb layer. Based on our experiment with rare-earth metals we discuss required properties for functional transducers, which may allow for novel field-control of the emitted picosecond strain pulses.
Research within the framework of Basic Psychological Need Theory (BPNT) finds strong associations between basic need frustration and depressive symptoms. This study examined the role of rumination as an underlying mechanism in the association between basic psychological need frustration and depressive symptoms. A cross-sectional sample of N = 221 adults (55.2% female, mean age = 27.95, range = 18–62, SD = 10.51) completed measures assessing their level of basic psychological need frustration, rumination, and depressive symptoms. Correlational analyses and multiple mediation models were conducted. Brooding partially mediated the relation between need frustration and depressive symptoms. BPNT and Response Styles Theory are compatible and can further advance knowledge about depression vulnerabilities.
Development of self-concept and task interest has been shown to be affected by social comparison processes in a variety of cross-sectional studies. A potential explanation for these effects is an effect of social comparative performance feedback on an individual’s self-evaluation of performance, which in turn influences development of self-concept and task interest. There are, however, only few studies addressing this topic with experimental designs. This study was aimed at closing this research gap by experimentally manipulating social comparative performance. Feedback given was based on 2 × 2 experimental conditions: social position (high vs. low) and average performance of the reference group (high vs. low). Results show a strong effect of social position on self-evaluation of performance and smaller effects on self-concept and task interest.
Sulfur is an important element that is incorporated into many biomolecules in humans. The incorporation and transfer of sulfur into biomolecules is, however, facilitated by a series of different sulfurtransferases. Among these sulfurtransferases is the human mercaptopyruvate sulfurtransferase (MPST) also designated as tRNA thiouridine modification protein (TUM1). The role of the human TUM1 protein has been suggested in a wide range of physiological processes in the cell among which are but not limited to involvement in Molybdenum cofactor (Moco) biosynthesis, cytosolic tRNA thiolation and generation of H2S as signaling molecule both in mitochondria and the cytosol. Previous interaction studies showed that TUM1 interacts with the L-cysteine desulfurase NFS1 and the Molybdenum cofactor biosynthesis protein 3 (MOCS3). Here, we show the roles of TUM1 in human cells using CRISPR/Cas9 genetically modified Human Embryonic Kidney cells. Here, we show that TUM1 is involved in the sulfur transfer for Molybdenum cofactor synthesis and tRNA thiomodification by spectrophotometric measurement of the activity of sulfite oxidase and liquid chromatography quantification of the level of sulfur-modified tRNA. Further, we show that TUM1 has a role in hydrogen sulfide production and cellular bioenergetics.
Long COVID patients show symptoms, such as fatigue, muscle weakness and pain. Adequate diagnostics are still lacking. Investigating muscle function might be a beneficial approach. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially sensitive for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients and their recovery process. AF parameters of elbow and hip flexors were assessed in 17 patients at three time points (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~50% of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~99% and 100% of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three time points. Symptom intensity decreased significantly from pre to end. The findings revealed a substantially impaired maximal holding capacity in long COVID patients, which returned to normal function with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process
The Adaptive Force (AF) reflects the neuromuscular capacity to adapt to external loads during holding muscle actions and is similar to motions in real life and sports. The maximal isometric AF (AFisomax) was considered to be the most relevant parameter and was assumed to have major importance regarding injury mechanisms and the development of musculoskeletal pain. The aim of this study was to investigate the behavior of different torque parameters over the course of 30 repeated maximal AF trials. In addition, maximal holding vs. maximal pushing isometric muscle actions were compared. A side consideration was the behavior of torques in the course of repeated AF actions when comparing strength and endurance athletes. The elbow flexors of n = 12 males (six strength/six endurance athletes, non-professionals) were measured 30 times (120 s rest) using a pneumatic device. Maximal voluntary isometric contraction (MVIC) was measured pre and post. MVIC, AFisomax, and AFmax (maximal torque of one AF measurement) were evaluated regarding different considerations and statistical tests. AFmax and AFisomax declined in the course of 30 trials [slope regression (mean ± standard deviation): AFmax = −0.323 ± 0.263; AFisomax = −0.45 ± 0.45]. The decline from start to end amounted to −12.8% ± 8.3% (p < 0.001) for AFmax and −25.41% ± 26.40% (p < 0.001) for AFisomax. AF parameters declined more in strength vs. endurance athletes. Thereby, strength athletes showed a rather stable decline for AFmax and a plateau formation for AFisomax after 15 trials. In contrast, endurance athletes reduced their AFmax, especially after the first five trials, and remained on a rather similar level for AFisomax. The maximum of AFisomax of all 30 trials amounted 67.67% ± 13.60% of MVIC (p < 0.001, n = 12), supporting the hypothesis of two types of isometric muscle action (holding vs. pushing). The findings provided the first data on the behavior of torque parameters after repeated isometric–eccentric actions and revealed further insights into neuromuscular control strategies. Additionally, they highlight the importance of investigating AF parameters in athletes based on the different behaviors compared to MVIC. This is assumed to be especially relevant regarding injury mechanisms.
Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.