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Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.
- Klíčová slova
- dual mechanism, exploitation, exploration, krill herd, latency, stability,
- Publikační typ
- časopisecké články MeSH
Many cell control processes consist of networks of interacting elements that affect the state of each other over time. Such an arrangement resembles the principles of artificial neural networks, in which the state of a particular node depends on the combination of the states of other neurons. The lambda bacteriophage lysis/lysogeny decision circuit can be represented by such a network. It is used here as a model for testing the validity of a neural approach to the analysis of genetic networks. The model considers multigenic regulation including positive and negative feedback. It is used to simulate the dynamics of the lambda phage regulatory system; the results are compared with experimental observation. The comparison proves that the neural network model describes behavior of the system in full agreement with experiments; moreover, it predicts its function in experimentally inaccessible situations and explains the experimental observations. The application of the principles of neural networks to the cell control system leads to conclusions about the stability and redundancy of genetic networks and the cell functionality. Reverse engineering of the biochemical pathways from proteomics and DNA micro array data using the suggested neural network model is discussed.
The relationship between the fractions of protein secondary structural components as determined from X-ray crystallographic data by the procedures of Kabsch and Sander (KS) and of Levitt and Greer (LG) is analyzed by neural network analysis of these two tabulations of literature data. A linear relationship between the KS and LG reductions of X-ray data to secondary structure descriptors is demonstrated by a regression analysis of the relationships between these sets of structural parameters. Back-propagation neural network analysis was then used to derive equations for determination of the most probable fractions of beta-sheet, bend, turn, and "other" conformations given the fraction of alpha-helix in a globular protein. The deviation of the X-ray values for beta-sheet from that determined with these equations was shown to have a variance that exponentially decreased with increasing fraction of alpha-helix. A second neural network analysis showed that knowledge of both the alpha-helical and beta-sheet fractions in a protein significantly reduces the uncertainty in prediction of the other components of the secondary structure. These analyses provide insight into the nature of the data sets derived from crystal structures. Since these complications of crystal structure data are commonly used as reference information for quantitative evaluation of spectra (for example, FTIR, Raman, and electronic or vibrational circular dichroism) in terms of secondary structure, such internal correlations in the reference sets may have significant effects on the stability of spectroscopic analyses derived from them.
- MeSH
- difrakce rentgenového záření MeSH
- matematika MeSH
- neuronové sítě MeSH
- pravděpodobnost MeSH
- proteiny chemie MeSH
- sekundární struktura proteinů * MeSH
- Publikační typ
- časopisecké články MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
- Research Support, U.S. Gov't, P.H.S. MeSH
- srovnávací studie MeSH
- Názvy látek
- proteiny MeSH
Contemporary molecular biology deals with wide and heterogeneous sets of measurements to model and understand underlying biological processes including complex diseases. Machine learning provides a frequent approach to build such models. However, the models built solely from measured data often suffer from overfitting, as the sample size is typically much smaller than the number of measured features. In this paper, we propose a random forest-based classifier that reduces this overfitting with the aid of prior knowledge in the form of a feature interaction network. We illustrate the proposed method in the task of disease classification based on measured mRNA and miRNA profiles complemented by the interaction network composed of the miRNA-mRNA target relations and mRNA-mRNA interactions corresponding to the interactions between their encoded proteins. We demonstrate that the proposed network-constrained forest employs prior knowledge to increase learning bias and consequently to improve classification accuracy, stability and comprehensibility of the resulting model. The experiments are carried out in the domain of myelodysplastic syndrome that we are concerned about in the long term. We validate our approach in the public domain of ovarian carcinoma, with the same data form. We believe that the idea of a network-constrained forest can straightforwardly be generalized towards arbitrary omics data with an available and non-trivial feature interaction network. The proposed method is publicly available in terms of miXGENE system (http://mixgene.felk.cvut.cz), the workflow that implements the myelodysplastic syndrome experiments is presented as a dedicated case study.
- Klíčová slova
- Domain knowledge, Machine learning, Omics data, Random forest, Regularization, microRNA,
- MeSH
- genové regulační sítě MeSH
- lidé MeSH
- messenger RNA genetika MeSH
- mikro RNA genetika MeSH
- umělá inteligence MeSH
- výpočetní biologie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- messenger RNA MeSH
- mikro RNA MeSH
Dissociated states represent pathological conditions where psychological trauma may emerge in a variety of forms such as psychic dissociative symptoms (hallucinations, derealization etc.) or on the other hand as somatoform symptoms (paroxysms, loss of motor control, involuntary movements etc.). Recent findings suggest that neurophysiological level of dissociative phenomena may be linked to the same neurophysiological principles that emerge in multi-stable perception of ambiguous stimuli likely caused by competing interpretations with mutual exclusivity. At this time there is evidence that temporal lobe seizure activity can produce dissociative syndrome and from these findings may be inferred that temporal lobe epileptic activity existing independently of neurological focal may share common neurobiological mechanism with dissociative symptoms. This conceptualization of dissociative phenomena is also in accordance with findings that originate from the study of the relationship between epilepsy and mental illness. The relationship was for the first time described in Meduna's concept of antagonism between epilepsy and psychosis and from the study of forced normalization introduced by Landolt in 1950s. The findings reported similar pathological conditions as in dissociative states when psychopathological symptoms and paroxysms may represent two different forms of the pathological process. Following the concept of forced normalization Tellenbach in 1965 introduced the term alternative psychosis implicating that stopping seizures does not mean vanishing or inactivity of the pathological state and that the epilepsy is still active subcortically and supplies energy for psychopathological symptoms. In the present review chaos in brain neural networks as a possible explanation of the relationship between dissociation and epileptic activity has been suggested that represents testable hypothesis for future research.
- MeSH
- disociační poruchy etiologie patofyziologie psychologie MeSH
- epilepsie temporálního laloku komplikace MeSH
- lidé MeSH
- mozek fyziologie MeSH
- neuronové sítě MeSH
- paměť MeSH
- psychotické poruchy patofyziologie MeSH
- vědomí MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
Drought stress limits plant growth and productivity. It triggers many responses by inducing changes in plant morphology and physiology. KDML105 rice is a key rice variety in Thailand and is normally grown in the northeastern part of the country. The chromosome segment substitution lines (CSSLs) were developed by transferring putative drought tolerance loci (QTLs) on chromosome 1, 3, 4, 8, or 9 into the KDML105 rice genome. CSSL104 is a drought-tolerant line with higher net photosynthesis and leaf water potential than KDML105 rice. The analysis of CSSL104 gene regulation identified the loci associated with these traits via gene co-expression network analysis. Most of the predicted genes are involved in the photosynthesis process. These genes are also conserved in Arabidopsis thaliana. Seven genes encoding chloroplast proteins were selected for further analysis through characterization of Arabidopsis tagged mutants. The response of these mutants to drought stress was analyzed daily for seven days after treatment by scoring green tissue areas via the PlantScreen™ XYZ system. Mutation of these genes affected green areas of the plant and stability index under drought stress, suggesting their involvement in drought tolerance.
- Klíčová slova
- CSSLs, co-expression network, drought stress, ‘KDML105’ rice,
- MeSH
- chromozomy rostlin genetika MeSH
- fyziologická adaptace * MeSH
- genové regulační sítě MeSH
- lokus kvantitativního znaku * MeSH
- období sucha * MeSH
- regulace genové exprese u rostlin * MeSH
- rostlinné proteiny genetika MeSH
- rýže (rod) genetika růst a vývoj MeSH
- stanovení celkové genové exprese MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- rostlinné proteiny MeSH
This research introduces a new technique to control constrained nonlinear systems, named Lyapunov-based neural network model predictive control using a metaheuristic optimization approach. This controller utilizes a feedforward neural network model as a prediction model and employs the driving training based optimization algorithm to resolve the related constrained optimization problem. The proposed controller relies on the simplicity and accuracy of the feedforward neural network model and the convergence speed of the driving training based optimization algorithm. The closed-loop stability of the developed controller is ensured by including the Lyapunov function as a constraint in the cost function. The efficiency of the suggested controller is illustrated by controlling the angular speed of three-phase squirrel cage induction motor. The reached results are contrasted to those of other methods, specifically the fuzzy logic controller optimized by teaching learning-based optimization algorithm, the optimized PID with particle swarm optimization algorithm, the neural network model predictive controller based on particle swarm optimization algorithm, and the neural network model predictive controller using driving training based optimization algorithm. This comparative study showcase that the suggested controller provides good accuracy, quickness and robustness due to the obtained values of the mean absolute error, mean square error root mean square error, enhancement percentage, and computing time in the different simulation cases, and it can be efficiently utilized to control constrained nonlinear systems with fast dynamics.
- Klíčová slova
- Constraints, DTBO, Lyapunov function, Metaheuristic, Model predictive control, Neural network, Nonlinear system, Squirrel cage induction motor,
- Publikační typ
- časopisecké články MeSH
Effects of xanthan gum (XG) addition and oil contents on the structural and rheological properties of Pickering emulsion stabilized by xanthan gum/Lysozyme nanoparticles (XG/Ly NPs) were analyzed by microstructure, creaming index, and rheological analysis. The results showed that XG addition reduced the droplet size of the emulsion, and a denser three-dimensional network structure was formed between droplets in the continuous phase. Thus, the migration of droplets slowed down, and the stability of Pickering emulsion increased. Rheological studies indicated that the network structure of Pickering emulsion depends on XG addition and oil content. The critical strain (γco) displayed three regimes. For low oil content (20%), γco decreased with the increase of XG concentration. For Pickering emulsion with medium oil content (40%, v/v), γco increased with increasing addition of XG. When high oil content (60-80%) was provided, γco was almost independent of XG addition. The results showed that the microstructure, stability and rheological properties of Pickering emulsion stabilized by XG/Ly NPs could be regulated by XG addition and oil content. This attempt provided theoretical support for regulating Pickering emulsion properties by polysaccharides addition, and established Pickering emulsions with various demands.
- Klíčová slova
- Microstructure, Pickering emulsion, Stability, rheology, Xanthan gum,
- MeSH
- bakteriální polysacharidy chemie MeSH
- emulze chemie MeSH
- muramidasa metabolismus MeSH
- nanočástice chemie ultrastruktura MeSH
- reologie * MeSH
- velikost částic MeSH
- viskozita MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- bakteriální polysacharidy MeSH
- emulze MeSH
- muramidasa MeSH
- xanthan gum MeSH Prohlížeč
Maintenance of genome stability is essential for every living cell as genetic information is repeatedly challenged during DNA replication in each cell division event. Errors, defects, delays, and mistakes that arise during mitosis or meiosis lead to an activation of DNA repair processes and in case of their failure, programmed cell death, i.e. apoptosis, could be initiated. Fam208a is a protein whose importance in heterochromatin maintenance has been described recently. In this work, we describe the crucial role of Fam208a in sustaining the genome stability during the cellular division. The targeted depletion of Fam208a in mice using CRISPR/Cas9 leads to embryonic lethality before E12.5. We also used the siRNA approach to downregulate Fam208a in zygotes to avoid the influence of maternal RNA in the early stages of development. This early downregulation increased arresting of the embryonal development at the two-cell stage and occurrence of multipolar spindles formation. To investigate this further, we used the yeast two-hybrid (Y2H) system and identified new putative interaction partners Gpsm2, Amn1, Eml1, Svil, and Itgb3bp. Their co-expression with Fam208a was assessed by qRT-PCR profiling and in situ hybridisation [1] in multiple murine tissues. Based on these results we proposed that Fam208a functions within the HUSH complex by interaction with Mphosph8 as these proteins are not only able to physically interact but also co-localise. We are bringing new evidence that Fam208a is multi-interacting protein affecting genome stability on the level of cell division at the earliest stages of development and also by interaction with methylation complex in adult tissues. In addition to its epigenetic functions, Fam208a appears to have an additional role in zygotic division, possibly via interaction with newly identified putative partners Gpsm2, Amn1, Eml1, Svil, and Itgb3bp.
- Klíčová slova
- Fam208a, Genome stability, HUSH, Multipolar spindle apparatus,
- MeSH
- aparát dělícího vřeténka metabolismus MeSH
- buněčné dělení genetika fyziologie MeSH
- CRISPR-Cas systémy MeSH
- embryonální vývoj genetika fyziologie MeSH
- fosfoproteiny metabolismus MeSH
- HEK293 buňky MeSH
- jaderné proteiny fyziologie MeSH
- letální geny MeSH
- lidé MeSH
- malá interferující RNA genetika farmakologie MeSH
- multiproteinové komplexy MeSH
- myši inbrední C57BL MeSH
- myši knockoutované MeSH
- myši MeSH
- nestabilita genomu MeSH
- RNA interference MeSH
- vývojová regulace genové exprese * MeSH
- zvířata MeSH
- zygota metabolismus MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- odvolaná publikace MeSH
- práce podpořená grantem MeSH
- Názvy látek
- Fam208a protein, mouse MeSH Prohlížeč
- fosfoproteiny MeSH
- jaderné proteiny MeSH
- malá interferující RNA MeSH
- MPHOSPH8 protein, human MeSH Prohlížeč
- Mphosph8 protein, mouse MeSH Prohlížeč
- multiproteinové komplexy MeSH
- TASOR protein, human MeSH Prohlížeč
Dna2 is an essential nuclease-helicase that acts in several distinct DNA metabolic pathways including DNA replication and recombination. To balance these functions and prevent unscheduled DNA degradation, Dna2 activities must be regulated. Here we show that Saccharomyces cerevisiae Dna2 function is controlled by sumoylation. We map the sumoylation sites to the N-terminal regulatory domain of Dna2 and show that in vitro sumoylation of recombinant Dna2 impairs its nuclease but not helicase activity. In cells, the total levels of the non-sumoylatable Dna2 variant are elevated. However, non-sumoylatable Dna2 shows impaired nuclear localization and reduced recruitment to foci upon DNA damage. Non-sumoylatable Dna2 reduces the rate of DNA end resection, as well as impedes cell growth and cell cycle progression through S phase. Taken together, these findings show that in addition to Dna2 phosphorylation described previously, Dna2 sumoylation is required for the homeostasis of the Dna2 protein function to promote genome stability.
- Klíčová slova
- DNA, Genomic instability,
- MeSH
- DNA fungální genetika metabolismus MeSH
- DNA-helikasy chemie genetika metabolismus MeSH
- fosforylace MeSH
- kinetika MeSH
- metabolické sítě a dráhy MeSH
- poškození DNA MeSH
- proteinové domény MeSH
- rekombinantní fúzní proteiny chemie genetika metabolismus MeSH
- replikace DNA MeSH
- Saccharomyces cerevisiae - proteiny chemie genetika metabolismus MeSH
- Saccharomyces cerevisiae enzymologie genetika růst a vývoj MeSH
- stabilita enzymů MeSH
- sumoylace MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- DNA fungální MeSH
- DNA-helikasy MeSH
- DNA2 protein, S cerevisiae MeSH Prohlížeč
- rekombinantní fúzní proteiny MeSH
- Saccharomyces cerevisiae - proteiny MeSH
- Siz2 protein, S cerevisiae MeSH Prohlížeč
