Phase Transition Analysis of the Dynamic Instability of Microtubules

This paper provides the phase transition analysis of a reaction diffusion equations system modeling dynamic instability of microtubules. For this purpose we have generalized the macroscopic model studied by Mour\~ao et all [MSS]. This model investigates the interaction between the microtubule nucleation, essential dynamics parameters and extinction and their impact on the stability of the system. The considered framework encompasses a system of partial differential equations for the elongation and shortening of microtubules, where the rates of elongation as well as the lifetimes of the elongating shortening phases are linear functions of GTP-tubulin concentration. In a novel way, this paper investigates the stability analysis and provides a bifurcation analysis for the dynamic instability of microtubules in the presence of diffusion and all of the fundamental dynamics parameters. Our stability analysis introduces the phase transition method as a new mathematical tool in the study of microtubule dynamics. The mathematical tools introduced to handle the problem should be of general use

Physics Informed Recurrent Neural Networks for Seismic Response Evaluation of Nonlinear Systems

Dynamic response evaluation in structural engineering is the process of determining the response of a structure, such as member forces, node displacements, etc when subjected to dynamic loads such as earthquakes, wind, or impact. This is an important aspect of structural analysis, as it enables engineers to assess structural performance under extreme loading conditions and make informed decisions about the design and safety of the structure. Conventional methods for dynamic response evaluation involve numerical simulations using finite element analysis (FEA), where the structure is modeled using finite elements, and the equations of motion are solved numerically. Although effective, this approach can be computationally intensive and may not be suitable for real-time applications. To address these limitations, recent advancements in machine learning, specifically artificial neural networks, have been applied to dynamic response evaluation in structural engineering. These techniques leverage large data sets and sophisticated algorithms to learn the complex relationship between inputs and outputs, making them ideal for such problems. In this paper, a novel approach is proposed for evaluating the dynamic response of multi-degree-of-freedom (MDOF) systems using physics-informed recurrent neural networks. The focus of this paper is to evaluate the seismic (earthquake) response of nonlinear structures. The predicted response will be compared to state-of-the-art methods such as FEA to assess the efficacy of the physics-informed RNN model

Drug Repurposing for Age-Related Macular Degeneration (AMD) Based on Gene Co-Expression Network Analysis

Background: Age-related macular degeneration (AMD) is a lesser-known eye disease in the world that gradually destroys a person’s vision by creating dark spots in the center of vision. Material and Methods: Samples of AMD-related genes were extracted from the NCBI, then the gene expression network (GCN) was extracted. In addition, pathway enrichment analysis was performed to investigate the role of co-expressed genes in AMD. Finally, the drug-gene interaction network was plotted.Results: The results of this work based on bioinformatics showed that many genes are involved in AMD disease, the most important of which are the genes of TYROBP, LILRB2, LCP2, PTPRC, CFH, SPARC, HTR5A.Overexpression of these genes can be considered as basic biomarkers for this disease, we separated some of which we had from the gene co-expression network and some from the results of genes ontology (genes that have a P value ≤ 0.05). The most important drugs were isolated from the drug-gene network based on degree, which included 5 drugs including ocriplasmin, collagenase clostridium histolyticum, topiramate, primidone, butalbital.Conclusion: Among the genes we found, three genes of CFH, TYROBP, SPARC seem to be more important than the others. Among drugs, ocriplasmin, topiramate, primidone can play a more important role based on the degree in the drug-gene network, because all steps are performed with different bioinformatics methods, clinical trials must confirm or reject the results.Keywords: Age-Related Macular Degeneration; AMD; Co-Expression Network; Drug Repurposing

Anti-Cancer Drugs Effective in Retinoblastoma: Based on a Protein-Protein Interaction Network

Background: This paper investigates the effects of potential drugs on differentially expressed genes (DEGs) associated with substantial alterations in retinoblastoma malignancy.Material and Methods: The GSE125903 dataset consisting of ten samples was used in this study (seven cancer patients and three control samples). The genes were ordered according to their adjusted p value, and 2000 top differential expressed genes with adj p values less than 0.01 were chosen as statistically significant. The STRING database version 11.0 was used to display the interaction among genes. The Cytoscape3.8.2 and the Clusterviz plugin software were used to construct the modules for the PPI network, and five clusters of genes were formed. The DGIdb v4.2.0 database was used to study drug-gene interactions and identify potentially beneficial medicines for retinoblastoma malignancy. The DAVID v.6.8 database was used to study gene ontology (GO) and important biological pathways.Results: CISPLATIN, TAMOXIFEN, and CYCLOPHOSPHAMIDE are the medicines that have been shown to be successful in treating retinoblastoma in our study. Additionally, we conducted a research on three other drugs: GEMCITABINE, OLAPARIB, and MITOXANTRONE. Although it is used to treat other diseases, it seems to have no apparent effects on retinoblastoma cancer treatment.Conclusion: CISPLATIN, a drug that causes apoptosis in tumors, has been proven to be the most effective therapy for retinoblastoma and should be included in treatment regimens for this illness. Of course, we obtained this information based on bioinformatics techniques, and more clinical trials are needed for more reliable results.Keywords: Protein-Protein Interaction Network; Retinoblastoma; Anti-Cancer

Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Firn Layers in Radar Echograms

Echograms created from airborne radar sensors capture the profile of firn layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate the snow accumulation rates, which are required to investigate the contribution of polar ice cap melt to sea level rise. However, automatically processing the radar echograms to detect the underlying firn layers is a challenging problem. In our work, we develop wavelet-based multi-scale deep learning architectures for these radar echograms to improve firn layer detection. We show that wavelet based architectures improve the optimal dataset scale (ODS) and optimal image scale (OIS) F-scores by 3.99% and 3.7%, respectively, over the non-wavelet architecture. Further, our proposed Skip-WaveNet architecture generates new wavelets in each iteration, achieves higher generalizability as compared to state-of-the-art firn layer detection networks, and estimates layer depths with a mean absolute error of 3.31 pixels and 94.3% average precision. Such a network can be used by scientists to trace firn layers, calculate the annual snow accumulation rates, estimate the resulting surface mass balance of the ice sheet, and help project global sea level rise