Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

To investigate the association of commonly used systemic medications with glaucoma and intraocular pressure (IOP) in the European population. Meta-analysis of eleven population-based cohort studies of the European Eye Epidemiology (E3) consortium. A total of 143240 participants were included in the glaucoma analyses and 47177 participants in the IOP analyses. We examined associations of four categories of systemic medications (antihypertensive medications: beta-blockers, diuretics, calcium channel blockers [CCBs], alpha-agonists, angiotensin-converting-enzyme inhibitors, angiotensin II receptor blockers; lipid-lowering medications; antidepressants; antidiabetic medications) with glaucoma prevalence and IOP. Glaucoma ascertainment and IOP measurement method were according to individual study protocols. Multivariable regression analyses were carried out in each study and results were pooled using random effects meta-analyses. Associations with antidiabetic medications were examined in diabetic participants only. Glaucoma prevalence and IOP. In the meta-analyses of our maximally-adjusted multivariable models, use of CCBs was associated with a higher prevalence of glaucoma (odds ratio [OR] with corresponding 95% confidence interval [95% CI]: 1.23 [1.08 to 1.39]). This association was stronger for monotherapy of CCBs with direct cardiac effects (OR [95% CI]: 1.96 [1.23 to 3.12]). The use of other antihypertensive medications, lipid-lowering medications, antidepressants or antidiabetic medications were not clearly associated with glaucoma. Use of systemic beta-blockers was associated with a lower IOP (Beta [95% CI]: -0.33 [-0.57 to -0.08] mmHg). Monotherapy of both selective (Beta [95% CI]: -0.45 [-0.74 to -0.16] mmHg) and non-selective (Beta [95% CI]: -0.54 [-0.94 to -0.15] mmHg) systemic beta-blockers was associated with lower IOP. There was a suggestive association between use of high-ceiling diuretics and lower IOP (Beta [95% CI]: -0.30 [-0.47; -0.14] mmHg), but not when used as monotherapy. Use of other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were not associated with IOP. We identified a potentially harmful association between use of CCBs and glaucoma prevalence. Additionally, we observed and quantified the association of lower IOP with systemic beta-blocker use. Both findings are potentially important given that glaucoma patients frequently use systemic antihypertensive medications. Determining whether the CCB association is causal should be a research priority.

Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit (ICU) in relation to their health status helps healthcare management allocate appropriate resources and better plan for the future. This paper presents predictive models for the LOS of ICU patients from the MIMIC-IV database based on typical demographic and administrative data, as well as early vital signs and laboratory measurements collected on the first day of ICU stay. The goal of this study was to demonstrate a practical, stepwise approach to predicting patient’s LOS in the ICU using machine learning and early available typical clinical data. The results show that this approach significantly improves the performance of models for predicting actual LOS in a pragmatic framework that includes only data with short stays predetermined by a prior classification.

Background: Research shows a connection between stress and depression, but there is little differentiation between areas of stress, making it difficult to identify and address specific areas in the context of public health measures. We utilized a multi-dimensional approach to chronic stress to better understand the relationship between different areas of stress and depression. Methods: We conducted linear regression analyses and used data from a sub-sample of the LIFE-Adult-Study (N = 1008) to analyze the connection between nine different areas of chronic stress (TICS) and depression (CES-D). In the second analysis, we controlled for sociodemographic variables, personality, physical activity, and social support. Results: There was a significant positive association between the stress domains Excessive Demands from Work, Lack of Social Recognition, Social Isolation, and Chronic Worrying and depression and a significant negative association between Pressure to Perform and depression. After adding control variables, only Pressure to Perform, Social Isolation, and Chronic Worrying remained significant predictors. Conclusions: By focusing on the connections between chronic stress and depression, researchers can help identify the areas that matter most and contribute to the creation of meaningful and efficient interventions. On the basis of our results, measures for the prevention of depression that focus on the reduction of worrying and social isolation are recommended.

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.