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Involvement in sport and exercise not only provides participants with health benefits but can be an important aspect of living a meaningful life. The COVID-19 pandemic and the temporary cessation of public life in March/April/May 2020 came with restrictions, which probably also made it difficult, if not impossible, to participate in certain types of sport or exercise. Following the philosophical position that different types of sport and exercise offer different ways of "relating to the world," this study explored (dis)continuity in the type of sport and exercise people practiced during the pandemic-related lockdown, and possible effects on mood. Data from a survey of 601 adult exercisers, collected shortly after the COVID-19 outbreak in Finland, were analyzed. Approximately one third (35%) of the participants changed their "worldmaking" and shifted to "I-Nature"-type activities. We observed worse mood during the pandemic in those who shifted from "I-Me," compared to those who had preferred the "I-Nature" relation already before the pandemic and thus experienced continuity. The clouded mood of those experiencing discontinuity may be the result of a temporary loss of "feeling at home" in their new exercise life-world. However, further empirical investigation must follow, because the observed effect sizes were small.
Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitive inhibitory interactions. Finally, we show that CIRI can be employed to refine predictions about metabolite-protein interactions from a recently proposed PROMIS approach that employs metabolomics and proteomics profiles from size exclusion chromatography in E. coli to predict metaboliteprotein interactions. Altogether, CIRI fills a gap in cataloguing metabolite-protein interactions and can be used in directing future machine learning efforts to categorize the regulatory type of these interactions.
We have directly resolved in the present work the interfacial composition during and after the interactions of a saturated atmosphere of oil vapor with soluble surfactant solutions at a planar water/air interface for the first time. Experiments were conducted on interactions of hexane vapor with solutions of alkyltrimethylammonium bromides and sodium dodecyl sulfate to observe the balance between cooperativity and competition of the components at the interface.
In all cases, hexane adsorption was strongly enhanced by the presence of the surfactant, even at bulk surfactant concentrations four orders of magnitude below the critical micelle concentration. Cooperativity of the surfactant adsorption was observed only for sodium dodecyl sulfate at intermediate bulk concentrations, yet for all four systems, competition set in at higher concentrations, as hexane adsorption reduced the surfactant surface excess. The data fully supported the complete removal of hexane from the interface following venting of the system to remove the saturated atmosphere of oil vapor.
These results help to identify future experiments that would elaborate and could explain the cooperativity of surfactant adsorption, such as on cationic surfactants with short alkyl chains and a broader series of anionic surfactants. This work holds relevance for oil recovery applications with foam, where there is a gas phase saturated with oil vapor.
The human language processing mechanism assigns a structure to the incoming materials as they unfold. There is evidence that the parser prefers some attachment types over others; however, theories of sentence processing are still in dispute over the stage at which each source of information contributes to the parsing system. The present study aims to identify the nature of initial parsing decisions during sentence processing through manipulating attachment type and verbs' argument structure. To this end, we designed a self-paced reading task using globally ambiguous constructions in Dutch. The structures included double locative prepositional phrases (PPs) where the first PP could attach both to the verb (high attachment) and the noun preceding it (low attachment). To disambiguate the structures, we presented a visual context in the form of short animation clips prior to each reading task. Furthermore, we manipulated the argument structure of the sentences using 2- and 3-argument verbs. The results showed that parsing decisions were influenced by contextual cues depending on the argument structure of the verb. That is, the visual context overcame the preference for high attachment only in the case of 2-argument verbs, while this preference persisted in structures including 3-argument verbs as represented by longer reading times for the low attachment interpretations. These findings can be taken as evidence that our language processing system actively integrates information from linguistic and non-linguistic sources from the initial stages of analysis to build up meaning. We discuss our findings in light of serial and parallel models of sentence processing.
Describing the heterogeneous structure of forests is often challenging.
One possibility is to analyze forest biomass in different plots and to derive plot-based frequency distributions.
However, these frequency distributions depend on the plot size and thus are scale dependent.
This study provides insights about transferring them between scales. Understanding the effects of scale on distributions of biomass is particularly important for comparing information from different sources such as inventories, remote sensing and modeling, all of which can operate at different spatial resolutions. Reliable methods to compare results of vegetation models at a grid scale with field data collected at smaller scales are still missing.
The scaling of biomass and variables, which determine the forest biomass, was investigated for a tropical forest in Panama. Based on field inventory data from Barro Colorado Island, spanning 50 ha over 30 years, the distributions of aboveground biomass, biomass gain and mortality were derived at different spatial resolutions, ranging from 10 to 100 m. Methods for fitting parametric distribution functions were compared.
Further, it was tested under which assumptions about the distributions a simple stochastic simulation forest model could best reproduce observed biomass distributions at all scales. Also, an analytical forest model for calculating biomass distributions at equilibrium and assuming mortality as a white shot noise process was tested.
Scaling exponents of about 0.47 were found for the standard deviations of the biomass and gain distributions, while mortality showed a different scaling relationship with an exponent of 0.3. Lognormal and gamma distribution functions fitted with the moment matching estimation method allowed for consistent parameter transfers between scales. Both forest models (stochastic simulation and analytical solution) were able to reproduce observed biomass distributions across scales, when combined with the derived scaling relationships.
The study demonstrates a way of how to approach the scaling problem in model-data comparisons by providing a transfer relationship. Further research is needed for a better understanding of the mechanisms that shape the frequency distributions at the different scales.
The current study examined the impact of the Good Behavior Game (GBG) on the academic engagement (AE) and disruptive behavior (DB) of at-risk students' in a German inclusive primary school sample using behavioral progress monitoring.
A multiple baseline design across participants was employed to evaluate the effects of the GBG on 35 primary school students in seven classrooms from grade 1 to 3 (M-age = 8.01 years, SDage = 0.81 years).
The implementation of the GBG was randomly staggered by 2 weeks across classrooms. Teacher-completed Direct Behavior Rating (DBR) was applied to measure AE and DB. We used piecewise regression and a multilevel extension to estimate the individual case-specific treatment effects as well as the generalized effects across cases.
Piecewise regressions for each case showed significant immediate treatment effects for the majority of participants (82.86%) for one or both outcome measures.
The multilevel approach revealed that the GBG improved at-risk students' classroom behaviors generally with a significant immediate treatment effect across cases (for AE, B = 0.74, p < 0.001; for DB, B = -1.29, p < 0.001).
The moderation between intervention effectiveness and teacher ratings of students' risks for externalizing psychosocial problems was significant for DB (B = -0.07, p = 0.047) but not for AE.
Findings are consistent with previous studies indicating that the GBG is an appropriate classroom-based intervention for at-risk students and expand the literature regarding differential effects for affected students.
In addition, the study supports the relevance of behavioral progress monitoring and data-based decision-making in inclusive schools in order to evaluate the effectiveness of the GBG and, if necessary, to modify the intervention for individual students or the whole group.
The chaotic spatio-temporal electrical activity during life-threatening cardiac arrhythmias like ventricular fibrillation is governed by the dynamics of vortex-like spiral or scroll waves. The organizing centers of these waves are called wave tips (2D) or filaments (3D) and they play a key role in understanding and controlling the complex and chaotic electrical dynamics. Therefore, in many experimental and numerical setups it is required to detect the tips of the observed spiral waves. Most of the currently used methods significantly suffer from the influence of noise and are often adjusted to a specific situation (e.g. a specific numerical cardiac cell model). In this study, we use a specific type of deep neural networks (UNet), for detecting spiral wave tips and show that this approach is robust against the influence of intermediate noise levels. Furthermore, we demonstrate that if the UNet is trained with a pool of numerical cell models, spiral wave tips in unknown cell models can also be detected reliably, suggesting that the UNet can in some sense learn the concept of spiral wave tips in a general way, and thus could also be used in experimental situations in the future (ex-vivo, cell-culture or optogenetic experiments).
Evolutionary reduction of adult body size (miniaturization) has profound consequences for organismal biology and is an important subject of evolutionary research. Based on two individuals we describe a new, extremely miniaturized chameleon, which may be the world's smallest reptile species. The male holotype of Brookesia nana sp. nov. has a snout-vent length of 13.5 mm (total length 21.6 mm) and has large, apparently fully developed hemipenes, making it apparently the smallest mature male amniote ever recorded. The female paratype measures 19.2 mm snout-vent length (total length 28.9 mm) and a micro-CT scan revealed developing eggs in the body cavity, likewise indicating sexual maturity. The new chameleon is only known from a degraded montane rainforest in northern Madagascar and might be threatened by extinction. Molecular phylogenetic analyses place it as sister to B. karchei, the largest species in the clade of miniaturized Brookesia species, for which we resurrect Evoluticauda Angel, 1942 as subgenus name. The genetic divergence of B. nana sp. nov. is rather strong (9.914.9% to all other Evoluticauda species in the 16S rRNA gene). A comparative study of genital length in Malagasy chameleons revealed a tendency for the smallest chameleons to have the relatively largest hemipenes, which might be a consequence of a reversed sexual size dimorphism with males substantially smaller than females in the smallest species. The miniaturized males may need larger hemipenes to enable a better mechanical fit with female genitals during copulation. Comprehensive studies of female genitalia are needed to test this hypothesis and to better understand the evolution of genitalia in reptiles.
The desiccation of the Aral Sea represents one of the largest human-made environmental regional disasters. The salt- and toxin-enriched dried-out basin provides a natural laboratory for studying ecosystem functioning and rhizosphere assembly under extreme anthropogenic conditions.
Here, we investigated the prokaryotic rhizosphere communities of the native pioneer plant Suaeda acuminata (C.A.Mey.) Moq. in comparison to bulk soil across a gradient of desiccation (5, 10, and 40 years) by metagenome and amplicon sequencing combined with quantitative PCR (qPCR) analyses. The rhizosphere effect was evident due to significantly higher bacterial abundances but less diversity in the rhizosphere compared to bulk soil. Interestingly, in the highest salinity (5 years of desiccation), rhizosphere functions were mainly provided by archaeal communities.
Along the desiccation gradient, we observed a significant change in the rhizosphere microbiota, which was reflected by (i) a decreasing archaeon-bacterium ratio, (ii) replacement of halophilic archaea by specific plant-associated bacteria, i.e., Alphaproteobacteria and Actinobacteria, and (iii) an adaptation of specific, potentially plant-beneficial biosynthetic pathways.
In general, both bacteria and archaea were found to be involved in carbon cycling and fixation, as well as methane and nitrogen metabolism.
Analysis of metagenome-assembled genomes (MAGs) showed specific signatures for production of osmoprotectants, assimilatory nitrate reduction, and transport system induction.
Our results provide evidence that rhizosphere assembly by cofiltering specific taxa with distinct traits is a mechanism which allows plants to thrive under extreme conditions. Overall, our findings highlight a function-based rhizosphere assembly, the importance of plant-microbe interactions in salinated soils, and their exploitation potential for ecosystem restoration approaches.IMPORTANCE
The desertification of the Aral Sea basin in Uzbekistan and Kazakhstan represents one of the most serious anthropogenic environmental disasters of the last century. Since the 1960s, the world's fourth-largest inland body of water has been constantly shrinking, which has resulted in an extreme increase of salinity accompanied by accumulation of many hazardous and carcinogenic substances, as well as heavy metals, in the dried-out basin.
Here, we investigated bacterial and archaeal communities in the rhizosphere of pioneer plants by combining classic molecular methods with amplicon sequencing as well as metagenomics for functional insights.
By implementing a desiccation gradient, we observed (i) remarkable differences in the archaeon-bacterium ratio of plant rhizosphere samples, (ii) replacement of archaeal indicator taxa during succession, and (iii) the presence of specific, potentially plant-beneficial biosynthetic pathways in archaea present during the early stages.
In addition, our results provide hitherto-undescribed insights into the functional redundancy between plant-associated archaea and bacteria.
The desertification of the Aral Sea basin in Uzbekistan and Kazakhstan represents one of the most serious anthropogenic environmental disasters of the last century.
Since the 1960s, the world's fourth-largest inland body of water has been constantly shrinking, which has resulted in an extreme increase of salinity accompanied by accumulation of many hazardous and carcinogenic substances, as well as heavy metals, in the dried-out basin.
Lakes act as important sinks for inorganic and organic sediment components. However, investigations of sedimentary carbon budgets within glacial lakes are currently absent from Arctic Siberia. The aim of this paper is to provide the first reconstruction of accumulation rates, sediment and carbon budgets from a lacustrine sediment core from Lake Rauchuagytgyn, Chukotka (Arctic Siberia). We combined multiple sediment biogeochemical and sedimentological parameters from a radiocarbon-dated 6.5m sediment core with lake basin hydroacoustic data to derive sediment stratigraphy, sediment volumes and infill budgets. Our results distinguished three principal sediment and carbon accumulation regimes that could be identified across all measured environmental proxies including early Marine Isotope Stage 2 (MIS2) (ca. 29-23.4 ka cal BP), mid-MIS2-early MIS1 (ca. 23.4-11.69 ka cal BP) and the Holocene (ca. 11.69-present). Estimated organic carbon accumulation rates (OCARs) were higher within Holocene sediments (average 3.53 gOCm(-2) a(-1)) than Pleistocene sediments (average 1.08 gOCm(-2) a(-1)) and are similar to those calculated for boreal lakes from Quebec and Finland and Lake Baikal but significantly lower than Siberian thermokarst lakes and Alberta glacial lakes. Using a bootstrapping approach, we estimated the total organic carbon pool to be 0.26 +/- 0.02 Mt and a total sediment pool of 25.7 +/- 1.71 Mt within a hydroacoustically derived sediment volume of ca. 32 990 557m(3). The total organic carbon pool is substantially smaller than Alaskan yedoma, thermokarst lake sediments and Alberta glacial lakes but shares similarities with Finnish boreal lakes. Temporal variability in sediment and carbon accumulation dynamics at Lake Rauchuagytgyn is controlled predominantly by palaeoclimate variation that regulates lake ice-cover dynamics and catchment glacial, fluvial and permafrost processes through time. These processes, in turn, affect catchment and within-lake primary productivity as well as catchment soil development. Spatial differences compared to other lake systems at a trans-regional scale likely relate to the high-latitude, mountainous location of Lake Rauchuagytgyn.