Elevated pCO2 alters the interaction patterns and functional potentials of rearing seawater microbiota

2021 ◽  
pp. 117615
Author(s):  
Weichuan Lin ◽  
Jiaqi Lu ◽  
Huaiying Yao ◽  
Zhibin Lu ◽  
Yimin He ◽  
...  
1968 ◽  
Vol 32 (5, Pt.1) ◽  
pp. 575-578 ◽  
Author(s):  
Martha C. Frede ◽  
Donald B. Gautney ◽  
James C. Baxter

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Linlong Mu ◽  
Jianhong Lin ◽  
Zhenhao Shi ◽  
Xingyu Kang

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.


Shock Waves ◽  
2021 ◽  
Author(s):  
C. Garbacz ◽  
W. T. Maier ◽  
J. B. Scoggins ◽  
T. D. Economon ◽  
T. Magin ◽  
...  

AbstractThe present study aims at providing insights into shock wave interference patterns in gas flows when a mixture different than air is considered. High-energy non-equilibrium flows of air and $$\hbox {CO}_2$$ CO 2 –$$\hbox {N}_2$$ N 2 over a double-wedge geometry are studied numerically. The impact of freestream temperature on the non-equilibrium shock interaction patterns is investigated by simulating two different sets of freestream conditions. To this purpose, the SU2 solver has been extended to account for the conservation of chemical species as well as multiple energies and coupled to the Mutation++ library (Multicomponent Thermodynamic And Transport properties for IONized gases in C++) that provides all the necessary thermochemical properties of the mixture and chemical species. An analysis of the shock interference patterns is presented with respect to the existing taxonomy of interactions. A comparison between calorically perfect ideal gas and non-equilibrium simulations confirms that non-equilibrium effects greatly influence the shock interaction patterns. When thermochemical relaxation is considered, a type VI interaction is obtained for the $$\hbox {CO}_2$$ CO 2 -dominated flow, for both freestream temperatures of 300 K and 1000 K; for air, a type V six-shock interaction and a type VI interaction are obtained, respectively. We conclude that the increase in freestream temperature has a large impact on the shock interaction pattern of the air flow, whereas for the $$\hbox {CO}_2$$ CO 2 –$$\hbox {N}_2$$ N 2 flow the pattern does not change.


Author(s):  
Ellen Kristine Solbrekke Hansen

AbstractThis paper aims to give detailed insights of interactional aspects of students’ agency, reasoning, and collaboration, in their attempt to solve a linear function problem together. Four student pairs from a Norwegian upper secondary school suggested and explained ideas, tested it out, and evaluated their solution methods. The student–student interactions were studied by characterizing students’ individual mathematical reasoning, collaborative processes, and exercised agency. In the analysis, two interaction patterns emerged from the roles in how a student engaged or refrained from engaging in the collaborative work. Students’ engagement reveals aspects of how collaborative processes and mathematical reasoning co-exist with their agencies, through two ways of interacting: bi-directional interaction and one-directional interaction. Four student pairs illuminate how different roles in their collaboration are connected to shared agency or individual agency for merging knowledge together in shared understanding. In one-directional interactions, students engaged with different agencies as a primary agent, leading the conversation, making suggestions and explanations sometimes anchored in mathematical properties, or, as a secondary agent, listening and attempting to understand ideas are expressed by a peer. A secondary agent rarely reasoned mathematically. Both students attempted to collaborate, but rarely or never disagreed. The interactional pattern in bi-directional interactions highlights a mutual attempt to collaborate where both students were the driving forces of the problem-solving process. Students acted with similar roles where both were exercising a shared agency, building the final argument together by suggesting, accepting, listening, and negotiating mathematical properties. A critical variable for such a successful interaction was the collaborative process of repairing their shared understanding and reasoning anchored in mathematical properties of linear functions.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anastasia Ryzhkova ◽  
Alena Taskina ◽  
Anna Khabarova ◽  
Veniamin Fishman ◽  
Nariman Battulin

AbstractGeneration of mature red blood cells, consisting mainly of hemoglobin, is a remarkable example of coordinated action of various signaling networks. Chromatin condensation is an essential step for terminal erythroid differentiation and subsequent nuclear expulsion in mammals. Here, we profiled 3D genome organization in the blood cells from ten species belonging to different vertebrate classes. Our analysis of contact maps revealed a striking absence of such 3D interaction patterns as loops or TADs in blood cells of all analyzed representatives. We also detect large-scale chromatin rearrangements in blood cells from mammals, birds, reptiles and amphibians: their contact maps display strong second diagonal pattern, representing an increased frequency of long-range contacts, unrelated to TADs or compartments. This pattern is completely atypical for interphase chromosome structure. We confirm that these principles of genome organization are conservative in vertebrate erythroid cells.


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