scholarly journals Thermodynamic constraints on the diversity of microbial ecosystems

2021 ◽  
Author(s):  
Jacob Cook ◽  
Samraat Pawar ◽  
Robert G. Endres
Keyword(s):  
Free Energy ◽  
Energy Supply ◽  
Ecosystem Dynamics ◽  
Individual Species ◽  
Microbial Ecosystems ◽  
Initial Substrate ◽  
Equilibrium Reactions ◽  
Ecosystem Diversity ◽  
The Impact

Most biological processes are driven by non-equilibrium thermodynamics, but despite significant progress in theoretical ecology the constraints this places on ecosystem dynamics has been barely considered. Microbial ecosystems represent a natural place to begin this consideration, as many of the ways they interact, such as metabolite production and cross-feeding, can be described in thermodynamic terms. Previous work considered the impact of thermodynamics such as the rate-yield trade-off on individual species' competitive ability, but restrained from analyzing complex dynamical systems. To address this gap we developed a thermodynamic microbial consumer-resource model with fully reversible reaction kinetics, which allows direct consideration of free-energy dissipation. Using this model, we show that ecosystem diversity increases with supplied free energy, because greater availability of free energy allows for faster ecosystem development. Thus, when species from the initial community begin to go extinct more possible niches have been formed, facilitating increased diversity. Our model also shows that the inclusion of species utilising near-to-equilibrium reactions increases diversity under conditions of low free-energy supply. At low free energy supply thermodynamic interaction types reach comparable strength to the conventional (competition and facilitation) interactions yielding a more nuanced classification of interactions, and emphasising the key role thermodynamics plays in the dynamics. Though our model is valid for all microbial ecosystems where diversification from an initial substrate occurs it is of particular use when the initial substrate is recalcitrant (low-free energy).

Fire: plant functional types and patch mosaic burning in fire-prone ecosystems

2008 ◽  
Vol 32 (4) ◽  
pp. 421-437 ◽  
Author(s):  
H.D. Allen
Keyword(s):  
Landscape Heterogeneity ◽  
Plant Functional Types ◽  
Individual Species ◽  
Above Ground Biomass ◽  
Functional Types ◽  
Patch Mosaic ◽  
Disturbance Factor ◽  
In Fire ◽  
The Impact

Shared fire-survival and fire-persistence traits are found in taxonomically unrelated plant species that commonly grow in fire-prone ecosystems. Such traits include resprouting, after fire has killed the above-ground biomass, and postfire seed release after the death of individual plants. Classification of such traits has led to a change in focus from research on the impact of fire as a disturbance factor on individual species, towards research into plant functional types associated with fire. This has led to a better understanding of the timing and geographic evolution of such traits as either fire-adapted or as a selective response to other disturbance factors. The identification of fire-survival and fire-persistence traits in fire-prone ecosystems is the first focus of this paper. It is followed by a discussion of recent research which offers a critical reappraisal of patch mosaic burning as a means to increase landscape heterogeneity and biodiversity, including the role played by plant functional types in determining diversity. The fire-prone ecosystems of mediterranean-type shrublands and heathlands, savannas and grasslands, and boreal and other coniferous forests are the main geographic focus of the paper.

ON THE CLASSIFICATION OF DIGITAL TECHNOLOGIES IN CIVIL PROCESS

2019 ◽  
pp. 27-35
Author(s):  
Alexandr Neznamov
Keyword(s):  
Information Processing ◽  
Scientific Community ◽  
Digital Technologies ◽  
Significant Feature ◽  
Legal Proceedings ◽  
Civil Process ◽  
Civil Proceedings ◽  
Processing Information ◽  
The Impact

Digital technologies are no longer the future but are the present of civil proceedings. That is why any research in this direction seems to be relevant. At the same time, some of the fundamental problems remain unattended by the scientific community. One of these problems is the problem of classification of digital technologies in civil proceedings. On the basis of instrumental and genetic approaches to the understanding of digital technologies, it is concluded that their most significant feature is the ability to mediate the interaction of participants in legal proceedings with information; their differentiating feature is the function performed by a particular technology in the interaction with information. On this basis, it is proposed to distinguish the following groups of digital technologies in civil proceedings: a) technologies of recording, storing and displaying (reproducing) information, b) technologies of transferring information, c) technologies of processing information. A brief description is given to each of the groups. Presented classification could serve as a basis for a more systematic discussion of the impact of digital technologies on the essence of civil proceedings. Particularly, it is pointed out that issues of recording, storing, reproducing and transferring information are traditionally more «technological» for civil process, while issues of information processing are more conceptual.

Influence of internal and external risks on sustainability of financial system of Аltai krai

2018 ◽  
Vol 35 (4) ◽  
pp. 133-136
Author(s):  
R. N. Ibragimov
Keyword(s):  
Sustainable Development ◽  
Financial Stability ◽  
Management Strategy ◽  
Financial System ◽  
Risk Management Strategy ◽  
The Sustainable Development ◽  
The Stability ◽  
The Impact

The article examines the impact of internal and external risks on the stability of the financial system of the Altai Territory. Classification of internal and external risks of decline, affecting the sustainable development of the financial system, is presented. A risk management strategy is proposed that will allow monitoring of risks, thereby these measures will help reduce the loss of financial stability and ensure the long-term development of the economy of the region.

Fish diversity

2017 ◽  
Author(s):  
Derek Burton ◽  
Margaret Burton
Keyword(s):  
Life Style ◽  
Fish Diversity ◽  
Additional Species ◽  
The Impact ◽  
Current Species

Fish diversity is considered in terms of variety of their morphological, taxonomic, habitat and population attributes. Fish, with over 30, 000 current species, represent the largest group of vertebrates. The complexity of classification of a group of this size and antiquity, together with recognition of additional species, demands continuous ongoing revision. The impact of the recent fundamental changes in fish classification in 2016 is discussed. Life in water involves adaptations to widely different habitats which can result in physiological morphological and life-style variations which are reviewed.

scholarly journals Formation of an Export-Oriented Agricultural Economy and Regional Open Innovations

2021 ◽  
Vol 7 (1) ◽  
pp. 32
Author(s):  
Victor L. Shabanov ◽  
Marianna Ya Vasilchenko ◽  
Elena A. Derunova ◽  
Andrey P. Potapov
Keyword(s):  
Agricultural Economy ◽  
Institutional Management ◽  
Agricultural Exports ◽  
Modeling Methodology ◽  
Fixed Assets ◽  
Analysis Application ◽  
The Impact ◽  
The Relationship

The aim of the work is to find relevant indicators for assessing the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports using tools for modeling the impact of innovation and investment development on increasing production and export potential in the context of the formation of an export-oriented agricultural economy. The modeling methodology and the proposed estimating and forecasting tools for diagnosing and monitoring the state of sectoral and regional innovative agricultural systems are used to analyze the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports based on the construction of the classification of Russian regions by factors that aggregate these features to diagnose incongruence problems and to improve institutional management in regional innovative export-oriented agrosystems. Based on the results of the factor analysis application, an underestimated role of indicators of investment in agriculture, the intensity and efficiency of agricultural production, were established. Based on the results of the cluster analysis, the established five groups of regions were identified, with significant differences in the level of investment in agriculture, the volume of production of the main types of agricultural products, and the export and exported food. The research results are of practical value for use in improving institutional management when planning reforms and transformations of regional innovative agrosystems.

scholarly journals The North Caucasus Region as a Blind Spot in the “European Green Deal”: Energy Supply Security and Energy Superpower Russia

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 17
Author(s):  
José Antonio Peña-Ramos ◽  
Philipp Bagus ◽  
Dmitri Amirov-Belova
Keyword(s):  
European Union ◽  
Energy Supply ◽  
Blind Spot ◽  
Illustrative Case ◽  
The European Union ◽  
North Caucasus ◽  
Strong Argument ◽  
The North ◽  
The Impact ◽  
Energy Supply Security

The “European Green Deal” has ambitious aims, such as net-zero greenhouse gas emissions by 2050. While the European Union aims to make its energies greener, Russia pursues power-goals based on its status as a geo-energy superpower. A successful “European Green Deal” would have the up-to-now underestimated geopolitical advantage of making the European Union less dependent on Russian hydrocarbons. In this article, we illustrate Russian power-politics and its geopolitical implications by analyzing the illustrative case of the North Caucasus, which has been traditionally a strategic region for Russia. The present article describes and analyses the impact of Russian intervention in the North Caucasian secessionist conflict since 1991 and its importance in terms of natural resources, especially hydrocarbons. The geopolitical power secured by Russia in the North Caucasian conflict has important implications for European Union’s energy supply security and could be regarded as a strong argument in favor of the “European Green Deal”.

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

scholarly journals A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

2021 ◽  
Vol 11 (2) ◽  
pp. 535
Author(s):  
Mahbubunnabi Tamal
Keyword(s):  
Textural Features ◽  
Strongly Correlated ◽  
Segmentation Method ◽  
Diagnosis And Prognosis ◽  
Radiotracer Uptake ◽  
Positron Emission ◽  
Occurrence Matrix ◽  
The Impact ◽  
Robust To Noise

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

Sign in / Sign up

Export Citation Format

Share Document