Tropical forests are a key component of the global carbon cycle. Yet, there are still high uncertainties in forest carbon stock and flux estimates, notably because of their spatial and temporal variability across the tropics. Several upcoming spaceborne missions have been designed to address this gap. High-quality ground data are essential for accurate calibration/validation so that spaceborne biomass missions can reach their full potential in reducing uncertainties regarding forest carbon stocks and fluxes. The BIOMASS mission, a P-band SAR satellite from the European Space Agency (ESA), aims at improving carbon stock mapping and reducing uncertainty in the carbon fluxes from deforestation, forest degradation, and regrowth. In situ activities in support of the BIOMASS mission were carried out in French Guiana and Gabon during the TropiSAR and AfriSAR campaigns. During these campaigns, airborne P-band SAR, forest inventory, and lidar data were collected over six study sites. This paper describes the methods used for forest inventory and lidar data collection and analysis, and presents resulting plot estimates and aboveground biomass maps. These reference datasets along with intermediate products (e.g., canopy height models) can be accessed through ESA's Forest Observation System and the Dryad data repository and will be useful for BIOMASS but also to other spaceborne biomass missions such as GEDI, NISAR, and Tandem-L for calibration/validation purposes. During data quality control and analysis, prospects for reducing uncertainties have been identified, and this paper finishes with a series of recommendations for future tropical forest field campaigns to better serve the remote sensing community.

Background: As large language models (LLMs) are becoming increasingly integrated into different aspects of health care, questions about the implications for medical academic literature have begun to emerge. Key aspects such as authenticity in academic writing are at stake with artificial intelligence (AI) generating highly linguistically accurate and grammatically sound texts. Objective: The objective of this study is to compare human-written with AI-generated scientific literature in orthopedics and sports medicine. Methods: Five original abstracts were selected from the PubMed database. These abstracts were subsequently rewritten with the assistance of 2 LLMs with different degrees of proficiency. Subsequently, researchers with varying degrees of expertise and with different areas of specialization were asked to rank the abstracts according to linguistic and methodological parameters. Finally, researchers had to classify the articles as AI generated or human written. Results: Neither the researchers nor the AI-detection software could successfully identify the AI-generated texts. Furthermore, the criteria previously suggested in the literature did not correlate with whether the researchers deemed a text to be AI generated or whether they judged the article correctly based on these parameters. Conclusions: The primary finding of this study was that researchers were unable to distinguish between LLM-generated and human-written texts. However, due to the small sample size, it is not possible to generalize the results of this study. As is the case with any tool used in academic research, the potential to cause harm can be mitigated by relying on the transparency and integrity of the researchers. With scientific integrity at stake, further research with a similar study design should be conducted to determine the magnitude of this issue.

(1) Background: Digitization in hospital nursing promises to transform the organization of care processes and, therefore, provide relief to nurse staffing shortages. While technological solutions are advanced and application fields numerous, comprehensive implementation remains challenging. Nursing leadership is crucial to digital change processes. This vignette study examined the effects of the motives and values on nurses’ motivation to use innovative technologies. (2) Methods: We asked hospital nurses in an online vignette study to assess a fictitious situation about the introduction of digital technology. We varied the devices on the degree of novelty (tablet/smart glasses), addressed motives (intrinsic/extrinsic), and values (efficiency/patient orientation). (3) Results: The analysis included 299 responses. The tablet vignettes caused more motivation than those of the smart glasses (Z = −6.653, p < 0.001). The dataset did not show significant differences between intrinsic and extrinsic motives. The nursing leader was more motivating when emphasizing efficiency rather than patient orientation (Z = −2.995, p = 0.003). (4) Conclusions: The results suggest efficiency as a motive for using known digital technologies. The nursing staff’s willingness to use digital technology is generally high. Management actions can provide a structural framework and training so that nursing leaders can ensure their staff’s engagement in using also unknown devices.

(1) Background: The nursing profession is associated with various stereotypes. These social images or prejudices against specific groups can inhibit the personal growth of individuals, e.g., sociodemographic characteristics influence the social image of nurses. Based on the forward-looking topic digitization, we examined and discussed the influences of sociodemographic characteristics and motives of hospital nurses on technical readiness to gain insights into the digitization process in hospital nursing. (2) Methods: As part of an online survey on technical readiness among German hospital nurses, we particularly examined sociodemographic influences on technical readiness and the relationship between sociodemographic characteristics and professional motives. Furthermore, we included a qualitative analysis of optional comment fields. (3) Results: The analysis included 295 responses. Age and gender had a significant influence on technical readiness. Furthermore, the importance of motives differed between gender and age. The analysis of the comments produced three categories specifying our results: beneficial experiences, obstructive experiences and further conditions. (4) Conclusions: In general, the nurses showed high technical readiness. In order to gain high motivation for digitization and promote personal growth, special targeting and cooperation between gender and age groups can be beneficial. However, there are more sites at system level, such as funding, cooperation and consistence.

Biotic homogenization is a commonly observed response following conversion of native ecosystems to agriculture, but our mechanistic understanding of this process is limited for microbial communities. In the case of rapid environmental changes, inference of homogenization mechanisms may be confounded by the fact that only a minority of taxa is active at any given point. RNA- and DNA-based community inference may help to distinguish the active fraction of a community from inactive taxa. Using these two community inference methods, we asked how soil prokaryotic communities respond to land use change following transition from rainforest to agriculture in the Congo Basin. Our results indicate that the magnitude of community homogenization is larger in the RNA-inferred community than the DNA-inferred perspective. We show that as the soil environment changes, the RNA-inferred community structure tracks environmental variation and loses spatial structure. The DNA-inferred community loses its association with environmental variability. Homogenization of the DNA-inferred community appears to instead be driven by the range expansion of a minority of taxa shared between the forest and conversion sites, which is also seen in the RNA-inferred community. Our results suggest that complementing DNA-based surveys with RNA can provide unique perspectives on community responses to environmental change.