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We used structural topic modeling to analyze over 800,000 German tweets about COVID-19 to answer the questions: What patterns emerge in tweets as a response to a health crisis? And how do topics discussed change over time? The study leans on the goals associated with the health information seeking (GAINS) model, discerning whether a post aims at tackling and eliminating the problem (i.e., problem-focused) or managing the emotions (i.e., emotion-focused); whether it strives to maximize positive outcomes (promotion focus) or to minimize negative outcomes (prevention focus). The findings indicate four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Public communication is volatile over time, and a shift is evidenced from self-centered to community-centered topics within 4.5 weeks. Our study illustrates social media text mining's potential to quickly and efficiently extract public opinions and reactions. Monitoring fears and trending topics enable policymakers to rapidly respond to deviant behavior, like resistive attitudes toward containment measures or deteriorating physical health. Healthcare workers can use the insights to provide mental health services for battling anxiety or extensive loneliness from staying home.
A suitable vehicle for integration of bioactive plant constituents is proposed. It involves modification of proteins using phenolics and applying these for protection of labile constituents. It dissects the noncovalent and covalent interactions of beta-lactoglobulin with coffee-specific phenolics. Alkaline and polyphenol oxidase modulated covalent reactions were compared. Tryptic digestion combined with MALDI-TOF-MS provided tentative allocation of the modification type and site in the protein, and an in silico modeling of modified beta-lactoglobulin is proposed. The modification delivers proteins with enhanced antioxidative properties. Changed structural properties and differences in solubility, surface hydrophobicity, and emulsification were observed. The polyphenol oxidase modulated reaction provides a modified beta-lactoglobulin with a high antioxidative power, is thermally more stable, requires less energy to unfold, and, when emulsified with lutein esters, exhibits their higher stability against UV light. Thus, adaptation of this modification provides an innovative approach for functionalizing proteins and their uses in the food industry.
This study addresses the interactions of coffee storage proteins with coffee-specific phenolic compounds. Protein profiles, of Coffea arabica and Coffea canephora (var robusta) were compared. Major Phenolic compounds were extracted and analyzed with appropriate methods. The polyphenol-protein interactions during protein extraction have been addressed by different analytical setups [reversed-phase high-performance liquid chromatography (RP-HPLC), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS), and Trolox equivalent antioxidant capacity (TEAC) assays], with focus directed toward identification of covalent adduct formation. The results indicate that C. arabica proteins are more susceptible to these interactions and the polyphenol oxidase activity seems to be a crucial factor for the formation of these addition products. A tentative allocation of the modification type and site in the protein has been attempted. Thus, the first available in silico modeling of modified coffee proteins is reported. The extent of these modifications may contribute to the structure and function of "coffee melanoidins" and are discussed in the context of coffee flavor formation.
To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes.
The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times.
Using the system, a single researcher can phenotype up to 7000 plants d(-1). Technical variance is very low (typically < 2%). We show quantitative results for the growth-impaired starch-excessmutant sex4-3 and the growth-enhancedmutant grf9.
We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions.
For around a decade, deep learning - the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers - modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources. identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of "Gartner Hype Curve", which in the general details describes a life cycle of modern technologies.
During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007–2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash–Sutcliffe efficiency of 0.72 and a Kling–Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century.
During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007-2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash-Sutcliffe efficiency of 0.72 and a Kling-Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century.
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.
Cyber-physical systems achieve sophisticated system behavior exploring the tight interconnection of physical coupling present in classical engineering systems and information technology based coupling. A particular challenging case are systems where these cyber-physical systems are formed ad hoc according to the specific local topology, the available networking capabilities, and the goals and constraints of the subsystems captured by the information processing part. In this paper we present a formalism that permits to model the sketched class of cyber-physical systems. The ad hoc formation of tightly coupled subsystems of arbitrary size are specified using a UML-based graph transformation system approach. Differential equations are employed to define the resulting tightly coupled behavior. Together, both form hybrid graph transformation systems where the graph transformation rules define the discrete steps where the topology or modes may change, while the differential equations capture the continuous behavior in between such discrete changes. In addition, we demonstrate that automated analysis techniques known for timed graph transformation systems for inductive invariants can be extended to also cover the hybrid case for an expressive case of hybrid models where the formed tightly coupled subsystems are restricted to smaller local networks.
In this work, an approach of paleoclimate reconstruction for tropical East Africa is presented. After giving a short summary of modern climate conditions in the tropics and the East African climate peculiarity, the potential of reconstructing climate from paleolake sediments is discussed. As demonstrated, the hydrologic sensitivity of high-elevated closed-basin lakes in the Central Kenya Rift yields valuable guaranties for the establishment of long-term climate records. Temporal fluctuations of the limnological characteristics saved in the lake sediments are used to define variations in the Quaternary climate history. Based on diatom analyses in radiocarbon- and 40Ar/39Ar-dated sediments, a chronology of paleoecologic fluctuations is developed for the Central Kenya Rift -lakes Nakuru, Elmenteita and Naivasha. At least during the penultimate interglacial (around 140 to 60 kyr BP) and during the last interglacial (around 12 to 4 kyr BP), these lakes experienced several transgression-regression cycles on time intervals of about 11,000 years. Additionally, a long-term trend of lake evolution is found suggesting the general succession from deep freshwater lakes towards more saline waters during the last million years. Using ecologic transfer functions and a simple lake-balance model, the observed paleohydrologic fluctuations are linked to potential precipitation-evaporation changes in the lake basins. Though also tectonic influences on the drainage pattern and the effect of varied seepage are investigated, it can be shown that already a small increase in precipitation of about 30±10 % may have affected the hydrologic budget of the intra-rift lakes within the reconstructed range. The findings of this study help to assess the natural climate variability of East Africa. They furthermore reflect the sensitivity of the Central Kenya Rift -lakes to fluctuations of large-scale climate parameters, such as solar radiation and sea-surface temperatures of the Indian Ocean.