Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Algorithm design of a combinatorial mathematical model for computer random signals.

ABSTRACT: To improve the processing effect of computer random signals, the manuscript employs the intelligent signal recognition algorithm to design a combinatorial mathematical model for computer random signals, and studies the parameter estimation of conventional frequency hopping signal (FHS) based on optimizing kernel function (KF). First, the mathematical form and graphical representation of the ambiguity function of the conventional FHS are explored. Furthermore, a new KF is presented according to its fuzzy function (FF) and the parameters of conventional FHSs are estimated according to the time-frequency distribution corresponding to the KF. Then, simulation experiments are carried out in different types of interference noise environments. The proposed combinatorial mathematical model for computer random signals shows a practical impact, and can effectively improve the effect of random signal combination.

SUBMITTER: Yao Q

PROVIDER: S-EPMC10909170 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Algorithm design of a combinatorial mathematical model for computer random signals.

Yao Qinghua Q Qiu Benhua B

PeerJ. Computer science 20240227

To improve the processing effect of computer random signals, the manuscript employs the intelligent signal recognition algorithm to design a combinatorial mathematical model for computer random signals, and studies the parameter estimation of conventional frequency hopping signal (FHS) based on optimizing kernel function (KF). First, the mathematical form and graphical representation of the ambiguity function of the conventional FHS are explored. Furthermore, a new KF is presented according to i ...[more]

PMID: 38435588

Similar Datasets

A Combinatorial Algorithm for Microbial Consortia Synthetic Design.

Project description:Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetize compounds of interest in a much more rapid and effective way by using other organisms than those naturally producing them. However, to thus engineer a single organism, often a microbe, to optimise one or a collection of metabolic tasks may lead to difficulties when attempting to obtain a production system that is efficient, or to avoid toxic effects for the recruited microorganism. The idea of using instead a microbial consortium has thus started being developed in the last decade. This was motivated by the fact that such consortia may perform more complicated functions than could single populations and be more robust to environmental fluctuations. Success is however not always guaranteed. In particular, establishing which consortium is best for the production of a given compound or set thereof remains a great challenge. This is the problem we address in this paper. We thus introduce an initial model and a method that enable to propose a consortium to synthetically produce compounds that are either exogenous to it, or are endogenous but where interaction among the species in the consortium could improve the production line.

| S-EPMC4931573 | biostudies-literature

Algorithm-supported, mass and sequence diversity-oriented random peptide library design.

Project description:Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.

| S-EPMC6437963 | biostudies-literature

Combinatorial Approach for Exploring Conformational Space and Activation Barriers in Computer-Aided Enzyme Design.

Project description:Computer-aided enzyme design is a field of great potential importance for biotechnological applications, medical advances, and a fundamental understanding of enzyme action. However, reaching a predictive ability in this direction is extremely challenging. It requires both the ability to predict quantitatively the activation barriers in cases where the structure and sequence are known and the ability to predict the effect of different mutations. In this work, we propose a protocol for predicting reasonable starting structures of mutants of proteins with known structures and for calculating the activation barriers of the generated mutants. Our approach also allows us to use the predicted structures of the generated mutant to predict structures and activation barriers for subsequent set of mutations. This protocol is used to examine the reliability of the in silico directed evolution of Kemp eliminase and haloalkane dehalogenase. We also used the results of single and double mutations as a base for predicting the effect of transition-state stabilization by multiple concurrent mutations. This strategy seems to be useful in creating an activity funnel that provides a qualitative ranking of the catalytic power of different mutants.

| S-EPMC8225234 | biostudies-literature

AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences.

Project description:Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function--essential to exert control over all polypeptide degrees of freedom--remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity.

| S-EPMC4881815 | biostudies-literature

A mathematical model of calcium signals around laser-induced epithelial wounds.

Project description:Cells around epithelial wounds must first become aware of the wound's presence in order to initiate the wound-healing process. An initial response to an epithelial wound is an increase in cytosolic calcium followed by complex calcium-signaling events. While these calcium signals are driven by both physical and chemical wound responses, cells around the wound will all be equipped with the same cellular components to produce and interact with the calcium signals. Here we have developed a mathematical model in the context of laser ablation of the Drosophila pupal notum that integrates tissue-level damage models with a cellular calcium-signaling toolkit. The model replicates experiments in the contexts of control wounds as well as knockdowns of specific cellular components, but it also provides new insights that are not easily accessible experimentally. The model suggests that cell-cell variability is necessary to produce calcium-signaling events observed in experiments; it quantifies calcium concentrations during wound-induced signaling events, and it shows that intercellular transfer of the molecule IP3 is required to coordinate calcium signals across distal cells around the wound. The mathematical model developed here serves as a framework for quantitative studies in both wound signaling and calcium signaling in the Drosophila system.

| S-EPMC10208100 | biostudies-literature

Dendritic signals command firing dynamics in a mathematical model of cerebellar Purkinje cells.

Project description:Dendrites of cerebellar Purkinje cells (PCs) respond to brief excitations from parallel fibers with lasting plateau depolarizations. It is unknown whether these plateaus are local events that boost the synaptic signals or they propagate to the soma and directly take part in setting the cell firing dynamics. To address this issue, we analyzed a likely mechanism underlying plateaus in three representations of a reconstructed PC with increasing complexity. Analysis in an infinite cable suggests that Ca plateaus triggered by direct excitatory inputs from parallel fibers and their mirror signals, valleys (putatively triggered by the local feed forward inhibitory network), cannot propagate. However, simulations of the model in electrotonic equivalent cables prove that Ca plateaus (resp. valleys) are conducted over the entire cell with velocities typical of passive events once they are triggered by threshold synaptic inputs that turn the membrane current inward (resp. outward) over the whole cell surface. Bifurcation analysis of the model in equivalent cables, and simulations in a fully reconstructed PC both indicate that dendritic Ca plateaus and valleys, respectively, command epochs of firing and silencing of PCs.

| S-EPMC2905124 | biostudies-literature

Numerical heat transfer simulation system based on Lagrangian particle mathematical model and SSPH algorithm

Project description: Not available

| S-EPMC11687755 | biostudies-literature

A computer-guided design tool to increase the efficiency of cellular conversions

Project description:Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.

2021-02-02 | GSE165961 | GEO

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice.

Project description:We introduce a general response model that allows for several simple restrictions, resulting in other models such as the extended Rasch model. For the extended Rasch model, a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values. To ensure comparability over time, a data augmentation method is used, which provides an augmented person-by-item data matrix and reproduces the sufficient statistics of the complete data matrix. Hence, longitudinal comparisons can be easily made based on simple summaries, such as proportion correct, sum score, etc. As an illustration of the method, an example is provided using data from a computer-adaptive practice mathematical environment.

| S-EPMC7003866 | biostudies-literature

Development of Random Forest Algorithm Based Prediction Model of Alzheimer's Disease Using Neurodegeneration Pattern.

Project description:ObjectiveAlzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).MethodsWe evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer's which is a commonly used segmentation software.ResultsOur RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer's (6-8 hours).ConclusionOur RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.

| S-EPMC7897872 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data