The Nrd1-Nab3-Sen1 (NNS) complex terminates transcription of non-coding RNA genes and mediates degradation of the produced transcript by the nuclear exosome. The NNS complex also represses some stress response genes, by stimulating premature termination. A well-characterized stress response in yeast is flocculation, where cells aggregate to form flocs under expression of lectin-encoding genes designated as FLOs. In this study, we demonstrated the role of the NNS complex and Rrp6p in the expression of flocculation genes: FLO1, FLO5, FLO9, and FLO10. Furthermore, a deletion mutant of the RNA processing machinery (RNT1), and SEN1 mutants that are unable to interact with Rnt1p, exhibit a flocculation phenotype. In summary, we have identified a cooperative role of Rnt1p, Rrp6p and the NNS complex in the repression of FLO genes.
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http://dx.doi.org/10.1016/j.febslet.2015.09.006 | DOI Listing |
ISA Trans
January 2025
College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, Hunan, China. Electronic address:
Approximation-free control effectively addresses uncertainty and disturbances without relying on approximation techniques such as fuzzy logic systems (FLS) and neural networks (NNs). However, singularity problems-where signals exceed preset boundaries under dynamic operating conditions-remain a challenge. This paper proposes an improved approximation-free control (I-AFC) method for the multi-agent system, which introduces a novel singularity compensator, providing a low-complexity design with exceptional adaptability while reducing the risk of singularity issues under changing working conditions (random initial values, system parameter variations, and changes in topology graph and followers' dynamics).
View Article and Find Full Text PDFJ Chem Phys
January 2025
Theoretical and Computational Physics Section, Raja Ramanna Centre for Advanced Technology, Indore 452013, India.
The orbital-free density functional theory (OF-DFT) based method is a convenient tool to carry out electronic structure calculations scaling almost linearly with the number of electrons. However, the main impediment in the application of this method is the unavailability of the accurate form for the non-interacting kinetic energy functional in terms of electron density. The Pauli kinetic energy functional is the unknown part of the kinetic energy functional, and the corresponding Pauli potential appears in the governing Euler equation.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Medical Sciences, University of Torino, Torino, Italy.
Classification and regression problems can be challenging when the relevant input features are diluted in noisy datasets, in particular when the sample size is limited. Traditional Feature Selection (FS) methods address this issue by relying on some assumptions such as the linear or additive relationship between features. Recently, a proliferation of Deep Learning (DL) models has emerged to tackle both FS and prediction at the same time, allowing non-linear modeling of the selected features.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Department of Biostatistics, School of Public Health, Virginia Commonwealth University, Richmond, VA, United States.
Background: The performance of a classification algorithm eventually reaches a point of diminishing returns, where the additional sample added does not improve the results. Thus, there is a need to determine an optimal sample size that maximizes performance while accounting for computational burden or budgetary concerns.
Objective: This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms.
Neural Netw
March 2025
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
This paper studies the asynchronous output feedback control and H synchronization problems for a class of continuous-time stochastic hidden semi-Markov jump neural networks (SMJNNs) affected by actuator saturation. Initially, a novel neural networks (NNs) model is constructed, incorporating semi-Markov process (SMP), hidden information, and Brownian motion to accurately simulate the complexity and uncertainty of real-world environments. Secondly, acknowledging system mode mismatches and the need for robust anti-interference capabilities, a non-fragile controller based on hidden information is proposed.
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