General Model
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2021 ◽  
Ines Sofia Calado Baptista ◽  
Vinodh Kandavalli ◽  
Vatsala Chauhan ◽  
Mohamed Nasurudeen Mohamed Bahrudeen ◽  
Bilena Lima de Brito Almeida ◽  

Escherichia coli uses the ability of σ factors to recognize specific DNA sequences in order to quickly control large gene cohorts. While most genes respond to only one s factor, approximately 5% have dual s factor preference. The ones in significant numbers are ‘σ 70+38 genes’, responsive to σ 70 , which controls housekeeping genes, as well as to σ 38 , which activates genes during stationary growth and stresses. We show that σ 70+38 genes are almost as upregulated in stationary growth as genes responsive to σ 38 alone. Also, their response strengths to σ 38 are predictable from their promoter sequences. Next, we propose a sequence- and σ 38 level-dependent, analytical model of σ 70+38 genes applicable in the exponential, stationary, and in the transition period between the two growth phases. Finally, we propose a general model, applicable to other σ factors as well. This model can guide the design of synthetic circuits with sequence-dependent sensitivity and plasticity to transitions between the exponential and stationary growth phases.

2021 ◽  
Vol 11 (1) ◽  
Giacomo Aletti ◽  
Irene Crimaldi

AbstractIn the existing literature about innovation processes, the proposed models often satisfy the Heaps’ law, regarding the rate at which novelties appear, and the Zipf’s law, that states a power law behavior for the frequency distribution of the elements. However, there are empirical cases far from showing a pure power law behavior and such a deviation is mostly present for elements with high frequencies. We explain this phenomenon by means of a suitable “damping” effect in the probability of a repetition of an old element. We introduce an extremely general model, whose key element is the update function, that can be suitably chosen in order to reproduce the behaviour exhibited by the empirical data. In particular, we explicit the update function for some Twitter data sets and show great performances with respect to Heaps’ law and, above all, with respect to the fitting of the frequency-rank plots for low and high frequencies. Moreover, we also give other examples of update functions, that are able to reproduce the behaviors empirically observed in other contexts.

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Jincheng Shi ◽  
Shengzhong Xiao

We are concerned with the global existence of classical solutions for a general model of viscosity long-short wave equations. Under suitable initial conditions, the existence of the global classical solutions for the viscosity long-short wave equations is proved. If it does not exist globally, the life span which is the largest time where the solutions exist is also obtained.

2021 ◽  
Vol 10 (1) ◽  
Zheng Kou ◽  
Yi-Fan Huang ◽  
Ao Shen ◽  
Saeed Kosari ◽  
Xiang-Rong Liu ◽  

Abstract Background Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Methods A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. Results The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. Conclusions Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic. Graphical Abstract

2021 ◽  
Vol 15 ◽  
Anush Ghambaryan ◽  
Boris Gutkin ◽  
Vasily Klucharev ◽  
Etienne Koechlin

Value-based decision making in complex environments, such as those with uncertain and volatile mapping of reward probabilities onto options, may engender computational strategies that are not necessarily optimal in terms of normative frameworks but may ensure effective learning and behavioral flexibility in conditions of limited neural computational resources. In this article, we review a suboptimal strategy – additively combining reward magnitude and reward probability attributes of options for value-based decision making. In addition, we present computational intricacies of a recently developed model (named MIX model) representing an algorithmic implementation of the additive strategy in sequential decision-making with two options. We also discuss its opportunities; and conceptual, inferential, and generalization issues. Furthermore, we suggest future studies that will reveal the potential and serve the further development of the MIX model as a general model of value-based choice making.

2021 ◽  
pp. 450-467
K. V. Samokhin

The monthly marriage and birth rate of the population of the Tambov province in 1915—1916 is considered in the article. The methodological basis of the work was the theory of modernization, which is considered by the author in the context of the history of Russia in the 19th — first half of the 20th centuries in the classical interpretation of the transition from traditional society to modern. The novelty of the article lies in the introduction into the scientific circulation of data on the seasonal dynamics of marriage and fertility of the Tambovites during the First World War. A comparative analysis of the corresponding numerical indicators among the townspeople and villagers in 1915—1916 with the pre-war period is carried out. The author comes to the conclusion that the seasonal dynamics of marriage and fertility can be used as a quantitative substantiation of such directions of spiritual modernization as the level of secularization and the propensity to innovate. The analysis shows that the general model of the demographic behavior of Tambov residents is generally correlated with the previous periods. The author comes to the conclusion that the revealed differences between the townspeople and villagers of the Tambov province in the studied plan indicate a greater inclination of the former to innovations and their higher level of secularization, and this confirms the position that the Tambov society was only at the initial stage of spiritual modernization.

2021 ◽  
Tao Buck ◽  
Courtney DiCocco ◽  
Jennifer L. Cuzzocreo ◽  
J. Adam Noah ◽  
Xian Zhang ◽  

The nexus model of social processing proposes that the right temporal parietal junction (rTPJ) serves as a neural hub for cognitive social functions. We test the hypothesis that the rTPJ is a domain general region including somatosensory social functions. Neuroimaging findings and cross-brain coherence for right- and left-hand handclasps with real vs. simulated hands were consistent with the domain general model.

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