Background The association between bivariate variables may not necessarily be homogeneous throughout the whole range of the variables. We present a new technique to describe inhomogeneity in the association of bivariate variables. Methods We consider the correlation of two normally distributed random variables. The 45° diagonal through the origin of coordinates represents the line on which all points would lie if the two variables completely agreed. If the two variables do not completely agree, the points will scatter on both sides of the diagonal and form a cloud. In case of a high association between the variables, the band width of this cloud will be narrow, in case of a low association, the band width will be wide. The band width directly relates to the magnitude of the correlation coefficient. We then determine the Euclidean distances between the diagonal and each point of the bivariate correlation, and rotate the coordinate system clockwise by 45°. The standard deviation of all Euclidean distances, named “global standard deviation” , reflects the band width of all points along the former diagonal. Calculating moving averages of the standard deviation along the former diagonal results in “locally structured standard deviations” and reflect patterns of “locally structured correlations (LSC)”. LSC highlight inhomogeneity of bivariate correlations. We exemplify this technique by analyzing the association between body mass index (BMI) and hip circumference (HC) in 6313 healthy East German adults aged 18 to 70 years. Results The correlation between BMI and HC in healthy adults is not homogeneous. LSC is able to identify regions where the predictive power of the bivariate correlation between BMI and HC increases or decreases, and highlights in our example that slim people have a higher association between BMI and HC than obese people. Conclusion Locally structured correlations (LSC) identify regions of higher or lower than average correlation between two normally distributed variables.
Carl Bergmann was an astute naturalist and physiologist. His ideas about animal size and shape were important advances in the pre-Darwinian nineteenth century. Bergmann’s rule claims that that in cold climates, large body mass increases the ratio of volume-to-surface area and provides for maximum metabolic heat retention in mammals and birds. Conversely, in warmer temperatures, smaller body mass increases surface area relative to volume and allows for greater heat loss. For humans, we now know that body size and shape are regulated more by social-economic-political-emotional (SEPE) factors as well as nutrition-infection interactions. Temperature has virtually no effect. Bergmann’s rule is a “just-so” story and should be relegated to teaching and scholarship about the history of science. That “rule” is no longer acceptable science and has nothing to tell us about physiological anthropology.
Background Intermittent hypoxia applied at rest or in combination with exercise promotes multiple beneficial adaptations with regard to performance and health in humans. It was hypothesized that replacing normoxia by moderate hyperoxia can increase the adaptive response to the intermittent hypoxic stimulus. Objective Our objective was to systematically review the current state of the literature on the effects of chronic intermittent hypoxia–hyperoxia (IHH) on performance- and health-related outcomes in humans. Methods PubMed, Web of Science™, Scopus, and Cochrane Library databases were searched in accordance with PRISMA guidelines (January 2000 to September 2021) using the following inclusion criteria: (1) original research articles involving humans, (2) investigation of the chronic effect of IHH, (3) inclusion of a control group being not exposed to IHH, and (4) articles published in peer-reviewed journals written in English. Results Of 1085 articles initially found, eight studies were included. IHH was solely performed at rest in different populations including geriatric patients ( n = 1), older patients with cardiovascular ( n = 3) and metabolic disease ( n = 2) or cognitive impairment ( n = 1), and young athletes with overtraining syndrome ( n = 1). The included studies confirmed the beneficial effects of chronic exposure to IHH, showing improvements in exercise tolerance, peak oxygen uptake, and global cognitive functions, as well as lowered blood glucose levels. A trend was discernible that chronic exposure to IHH can trigger a reduction in systolic and diastolic blood pressure. The evidence of whether IHH exerts beneficial effects on blood lipid levels and haematological parameters is currently inconclusive. A meta-analysis was not possible because the reviewed studies had a considerable heterogeneity concerning the investigated populations and outcome parameters. Conclusion Based on the published literature, it can be suggested that chronic exposure to IHH might be a promising non-pharmacological intervention strategy for improving peak oxygen consumption, exercise tolerance, and cognitive performance as well as reducing blood glucose levels, and systolic and diastolic blood pressure in older patients with cardiovascular and metabolic diseases or cognitive impairment. However, further randomized controlled trials with adequate sample sizes are needed to confirm and extend the evidence. This systematic review was registered on the international prospective register of systematic reviews (PROSPERO-ID: CRD42021281248) ( https://www.crd.york.ac.uk/prospero/ ).
Soft actuator performance can be tuned by chemistry or mechanical manipulation, but this adjustability is limited especially in view of their growing technological relevance. Inspired from textile engineering, we designed and fabricated fiber mesh actuators and introduced new features like anisotropic behavior and soft-tissue like elastic deformability. Design criteria for the meshes are the formation of fiber bundles, the angle between fiber bundles in different stacked layers and covalent crosslinks forming within and between fibers at their interfacial contact areas. Through crosslinking the interfiber bond strength increased from a bond transmitting neither axial nor rotational loads (pin joint) to a bond strength capable of both (welded joint). For non-linear elastic stiffening, stacked fiber bundles with four embracing fibers were created forming microstructural rhombus shapes. Loading the rhombus diagonally allowed generation of “soft tissue”-like mechanics. By adjustment of stacking angles, the point of strong increase in stress is tuned. While the highest stresses are observed in aligned and crosslinked fiber mats along the direction of the fiber, the strongest shape-memory actuation behavior is found in randomly oriented fiber mats. Fiber mesh actuators controlled by temperature are of high significance as soft robot skins and as for active patches supporting tissue regeneration.
Peatlands ecosystem is one of the largest global terrestrial carbon pools. However, there is a shortness of its characterisation and information through new proximal sensing approaches. The visible and near-infrared spectroscopy is an inexpensive, quick, non-evasive, proximal sensing and low-cost analysis employed in field and/or laboratory. Despite that, there is another current issue in using this tool for creating global models, which is how it can retrieve local characteristics such as soil organic carbon (SOC) and total nitrogen (TN) in peatlands ecosystems. The aims in this study were to: (i) create a local model for quantifying SOC and TN finding the best pre-processing and machine learning methods in peatlands ecosystem, and (ii) evaluate the contribution of SOC and TN data collected in that ecosystem to global models in European Union. The hypothesis was that the SOC and TN data sampled in peatlands ecosystem can improve analytical quantification of those soil properties. The soil and spectral datasets were retrieved from the Land Use/Cover Area frame Statistical Survey with 21,771 observations at 0–20 cm depth and 63 soil cores in a degraded peatland in Germany with 262 observations up to 2 m depth. We evaluated three spectral pre-processing techniques with the Partial Least Square Regression (PLSR), Random Forest (RF), and Cubist machine learning algorithms. The best pre-processing technique was achieved applying Savitzky-Golay smoothing with a window size of 71 points, 2nd order polynomial, and zero derivative with Cubist algorithm for both SOC and TN predictions. Furthermore, merging the local with global data for global modelling demonstrated to improve SOC and TN predictions because of the local data representativeness and quality. Therefore, the SOC and TN data sampled in peatlands ecosystem can improve quantification of those soil properties in field and laboratory, which are crucial proxies for GHG emissions and climate change.
Background/objective Negative emotional states, such as depression, anxiety, and stress challenge health care due to their long-term consequences for mental disorders. Accumulating evidence indicates that regular physical activity (PA) can positively influence negative emotional states. Among possible candidates, resilience and exercise tolerance in particular have the potential to partly explain the positive effects of PA on negative emotional states. Thus, the aim of this study was to investigate the association between PA and negative emotional states, and further determine the mediating effects of exercise tolerance and resilience in such a relationship. Method In total, 1117 Chinese college students (50.4% female, Mage=18.90, SD=1.25) completed a psychosocial battery, including the 21-item Depression Anxiety Stress Scale (DASS-21), the Connor-Davidson Resilience Scale (CD-RISC), the Preference for and Tolerance of the Intensity of Exercise Questionnaire (PRETIE-Q), and the International Physical Activity Questionnaire short form (IPAQ-SF). Regression analysis was used to identify the serial multiple mediation, controlling for gender, age and BMI. Results PA, exercise intensity-tolerance, and resilience were significantly negatively correlated with negative emotional states (Ps<.05). Further, exercise tolerance and resilience partially mediated the relationship between PA and negative emotional states. Conclusions Resilience and exercise intensity-tolerance can be achieved through regularly engaging in PA, and these newly observed variables play critical roles in prevention of mental illnesses, especially college students who face various challenges. Recommended amount of PA should be incorporated into curriculum or sport clubs within a campus environment.
Classical linguistic theory assumes that formal aspects, like sound, are not internally related to the meaning of words. However, recent research suggests language might code affective meaning such as threat and alert sublexically. Positing affective phonological iconicity as a systematic organization principle of the German lexicon, we calculated sublexical affective values for sub-syllabic phonological word segments from a large-scale affective lexical German database by averaging valence and arousal ratings of all words any phonological segment appears in. We tested word stimuli with either consistent or inconsistent mappings between lexical affective meaning and sublexical affective values (negative-valence/high-arousal vs. neutral-valence/low-arousal) in an EEG visual-lexical-decision task. A mismatch between sublexical and lexical affective values elicited an increased N400 response. These results reveal that systematic affective phonological iconicity – extracted from the lexicon - impacts the extraction of lexical word meaning during reading.
Hundreds of basaltic plateau margins east of the Patagonian Cordillera are undermined by numerous giant slope failures. However, the overall extent of this widespread type of plateau collapse remains unknown and incompletely captured in local maps. To detect giant slope failures consistently throughout the region, we train two convolutional neural networks (CNNs), AlexNet and U-Net, with Sentinel-2 optical data and TanDEM-X topographic data on elevation, surface roughness, and curvature. We validated the performance of these CNNs with independent testing data and found that AlexNet performed better when learned on topographic data, and UNet when learned on optical data. AlexNet predicts a total landslide area of 12,000 km² in a study area of 450,000 km², and thus one of Earth's largest clusters of giant landslides. These are mostly lateral spreads and rotational failures in effusive rocks, particularly eroding the margins of basaltic plateaus; some giant landslides occurred along shores of former glacial lakes, but are least prevalent in Quaternary sedimentary rocks. Given the roughly comparable topographic, climatic, and seismic conditions in our study area, we infer that basalts topping weak sedimentary rocks may have elevated potential for large-scale slope failure. Judging from the many newly detected and previously unknown landslides, we conclude that CNNs can be a valuable tool to detect large-scale slope instability at the regional scale. However, visual inspection is still necessary to validate results and correctly outline individual landslide source and deposit areas.
Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods. The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.
In this article we review the physical and chemical properties of methane (CH4) relevant to impacts on climate, ecosystems, and air pollution, and examine the extent to which this is reflected in climate and air pollution governance. Although CH4 is governed under the UNFCCC climate regime, its treatment there is limited to the ways in which it acts as a “CO2 equivalent” climate forcer on a 100-year time frame. The UNFCCC framework neglects the impacts that CH4 has on near-term climate, as well its impacts on human health and ecosystems, which are primarily mediated by methane’s role as a precursor to tropospheric ozone. Frameworks for air quality governance generally address tropospheric ozone as a pollutant, but do not regulate CH4 itself. Methane’s climate and air quality impacts, together with its alarming rise in atmospheric concentrations in recent years, make it clear that mitigation of CH4 emissions needs to be accelerated globally. We examine challenges and opportunities for further progress on CH4 mitigation within the international governance landscapes for climate change and air pollution.
Riverine ecosystems provide various ecosystem services. One of these services is the biological control of eutrophication by grazing macroinvertebrates. However, riverine ecosystems are subject to numerous stressors that affect community structure, functions, and stability properties. To manage rivers in response to these stressors, a better understanding of the ecological functions underlying services is needed. This requires consideration of local and regional processes, which requires a metacommunity approach that links local food webs through drift and dispersal. This takes into account long-distance interactions that can compensate for local effects of stressors. Our modular model MASTIFF (Multiple Aquatic STressors In Flowing Food webs) is stage-structured, spatially explicit, and includes coupled food webs consisting of benthic resource-consumer interactions between biofilm and three competing macroinvertebrate functional types. River segments are unidirectionally connected through organismal drift and bidirectionally connected through dispersal. Climate and land use stressors along the river can be accounted for. Biocontrol of biofilm eutrophication is used as an exemplary functional indicator. We present the model and the underlying considerations, and show in an exemplary application that explicit consideration of drift and dispersal is essential for understanding the spatiotemporal biocontrol of eutrophication. The combination of drift and dispersal reduced eutrophication events. While dispersal events were linked to specific periods in the species life cycles and therefore had limited potential to control, drift was ubiquitous and thus responded more readily to changing habitat conditions. This indicates that drift is an important factor for coping with stress situations. Finally, we outline and discuss the potential and possibilities of MASTIFF as a tool for mechanistic, cross-scale analyses of multiple stressors to advance knowledge of riverine ecosystem functioning.
To achieve the European Union's target for climate neutrality by 2050 reduced energy demand will make the transition process faster and cheaper. The role of policies that support energy efficiency measures and demand-side management practices will be critical and to ensure that energy demand models are relevant to policymakers and other end-users, understanding how to further improve the models and whether they are tailored to user needs to support efficient decision-making processes is crucial. So far though, no scientific studies have examined the key user needs for energy demand modelling in the context of the climate neutrality targets. In this article we address this gap using a multi-method approach based on empirical and desk research. Through survey and stakeholder meetings and workshops we identify user needs of different stakeholder groups, and we highlight the direction in which energy demand models need to be improved to be relevant to their users. Through a detailed review of existing energy demand models, we provide a full understanding of the key characteristics and capabilities of existing tools, and we identify their limitations and gaps. Our findings show that classical demand-related questions remain important to model users, while most of the existing models can answer these questions. Furthermore, we show that some of the user needs related to sectoral demand modelling, dictated by the latest policy developments, are under-researched and are not addressed by existing tools.
Plants show remarkable phenotypic plasticity and are able to adjust their morphology and development to diverse environmental stimuli. Morphological acclimation responses to elevated ambient temperatures are collectively termed thermomorphogenesis. In Arabidopsis thaliana, morphological changes are coordinated to a large extent by the transcription factor PHYTOCHROME-INTERACTING FACTOR 4 (PIF4), which in turn is regulated by several thermosensing mechanisms and modulators. Here, we review recent advances in the identification of factors that regulate thermomorphogenesis of Arabidopsis seedlings by affecting PIF4 expression and PIF4 activity. We summarize newly identified thermosensing mechanisms and highlight work on the emerging topic of organ- and tissue-specificity in the regulation of thermomorphogenesis.
Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and competencies. For example, research in assessing science teachers' noticing and attention to classroom events benefitted from more open-ended response formats because teachers can present their own accounts. Then, open-ended responses are typically analyzed with some form of content analysis. However, language is noisy, ambiguous, and unsegmented and thus open-ended, constructed responses are complex to analyze. Uncovering patterns in these responses would benefit from more principled and systematic analysis tools. Consequently, computer-based methods with the help of machine learning and natural language processing were argued to be promising means to enhance assessment of noticing skills with constructed reponse formats. In particular, pretrained language models recently advanced the study of linguistic phenomena and thus could well advance assessment of complex constructs through constructed response items. This study examines potentials and challenges of a pretrained language model-based clustering approach to assess preservice physics teachers' attention to classroom events as elicited through open-ended written descriptions. It was examined to what extent the clustering approach could identify meaningful patterns in the constructed responses, and in what ways textual organization of the responses could be analyzed with the clusters. Preservice physics teachers (N=75) were instructed to describe a standardized, video-recorded teaching situation in physics. The clustering approach was used to group related sentences. Results indicate that the pretrained language model-based clustering approach yields well interpretable, specific, and robust clusters, which could be mapped to physics-specific and more general contents. Furthermore, the clusters facilitate advanced analysis of the textual organization of the constructed responses. Hence, we argue that machine learning and natural language processing provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods.
Jean Baudrillards Beitrag zum Diskurs der Biopolitik ist bisher unberücksichtigt geblieben. Florian Cziesla rekonstruiert diesen im Anschluss an Martin Saars Überlegungen zur genealogischen Kritik sowie in kritischer Auseinandersetzung mit maßgeblichen Texten zur Biopolitik von Michel Foucault und Giorgio Agamben als Genealogie der politischen Ökonomie des Todes. Verbunden wird die als genealogische Intervention präsentierte Kritik Baudrillards schließlich mit Mike Hills und Warren Montags Reflexionen zur tödlichen Logik im Herzen liberaler und neoliberaler Theorie.
East Africa is a global biodiversity hotspot and exhibits distinct longitudinal diversity gradients from west to east in freshwater fishes and forest mammals. The assembly of this exceptional biodiversity and the drivers behind diversity gradients remain poorly understood, with diversification often studied at local scales and less attention paid to biotic exchange between Afrotropical regions. Here, we reconstruct a river system that existed for several millennia along the now semiarid Kenya Rift Valley during the humid early Holocene and show how this river system influenced postglacial dispersal of fishes and mammals due to its dual role as a dispersal corridor and barrier. Using geomorphological, geochronological, isotopic, and fossil analyses and a synthesis of radiocarbon dates, we find that the overflow of Kenyan rift lakes between 12 and 8 ka before present formed a bidirectional river system consisting of a “Northern River” connected to the Nile Basin and a “Southern River,” a closed basin. The drainage divide between these rivers represented the only viable terrestrial dispersal corridor across the rift. The degree and duration of past hydrological connectivity between adjacent river basins determined spatial diversity gradients for East African fishes. Our reconstruction explains the isolated distribution of Nilotic fish species in modern Kenyan rift lakes, Guineo-Congolian mammal species in forests east of the Kenya Rift, and recent incipient vertebrate speciation and local endemism in this region. Climate-driven rearrangements of drainage networks unrelated to tectonic activity contributed significantly to the assembly of species diversity and modern faunas in the East African biodiversity hotspot.
While scholars have argued that membership in Regional Organizations (ROs) can increase the likelihood of democratization, we see many autocratic regimes surviving in power albeit being members of several ROs. This article argues that this is the case because these regimes are often members in “Clubs of Autocrats” that supply material and ideational resources to strengthen domestic survival politics and shield members from external interference during moments of political turmoil. The argument is supported by survival analysis testing the effect of membership in autocratic ROs on regime survival between 1946 to 2010. It finds that membership in ROs composed of more autocratic member states does in fact raise the likelihood of regime survival by protecting incumbents against democratic challenges such as civil unrest or political dissent. However, autocratic RO membership does not help to prevent regime breakdown due to autocratic challenges like military coups, potentially because these types of threats are less likely to diffuse to other member states. The article thereby adds to our understanding of the limits of democratization and potential reverse effects of international cooperation, and contributes to the literature addressing interdependences of international and domestic politics in autocratic regimes.
Geothermal areas of Greece are located in regions affected by recent volcanism and in continental basins characterised by elevated heat flow. Many of them are found along the coast, and thus, water is often saline due to marine intrusion. In the current study, we present about 300 unpublished and literature data from thermal and cold mineral waters collected along Greece. Samples were analysed for major ions, Li, SiO 2 and isotopes in water. Measured temperatures range from 6.5 to 98 °C, pH from 1.96 to 11.98, while Total Dissolved Solutes (TDS) from 0.22 to 51 g/L. Waters were subdivided into four main groups: (1) thermal; (2) cold; (3) acidic (pH < 5); and (4) hyperalkaline (pH > 11). On statistical basis, thermal waters were subdivided into subgroups according to both their temperature [warm (< 29 °C), hypothermal (29–48 °C), thermal (48–75 °C) and hyperthermal (> 75 °C)] and TDS [low salinity (< 4 g/L), brackish (4–30 g/L) and saline (> 30 g/L)]. Cold waters were subdivided based on their p CO 2 [low (< 0.05 atm), medium (0.05–0.85 atm) and high (> 0.85 atm)]. δ ¹⁸ O–H 2 O ranges from − 12.7 to + 2.7‰ versus SMOW, while δ ² H–H 2 O from − 91 to + 12‰ versus SMOW being generally comprised between the Global Meteoric Water Line and the East Mediterranean Meteoric Water Line. Positive δ ¹⁸ O shifts with respect to the former are mostly related to mixing with seawater, while only for a few samples these shifts point to high-temperature water–rock interaction processes. Only a few thermal waters gave reliable geothermometric estimates, suggesting reservoir temperatures between 80 and 260 °C.
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