Eight-Chain and Full-Network Models and Their Modified Versions for Rubber Hyperelasticity: A Comparative Study

The eight-chain model, also known as Arruda-Boyce model, is widely used to capture the rate-independent hyperelastic response of rubber-like materials. The parameters of this model are physically based and explained from micromechanics of chain molecules. Despite its excellent performance with only two material parameters to capture bench measurements in uniaxial and pure shear regime, the model is known to be significantly deficient in predicting the equibiaxial data. To ameliorate such drawback, over the years, several modified versions of this successful model have been proposed in the literature. The so-called full-network model is another micromechanically motivated chain model, which has also few modified versions in the literature. For this study, two modified versions of the full-network model have been selected. In this contribution, five modified versions of the Arruda-Boyce model and two modified versions of full-network model are critically compared with the classical eight-chain model for their adequacy in representing equibiaxial data. To do a comparison of all selected models in reproducing the well-known Treloar data, the analytical expressions for the three homogeneous deformation modes, that is, uniaxial tension, equibiaxial tension, and pure shear have been derived and the performances of the selected models are analysed. The comparative study demonstrates that modified Flory-Erman model, Gornet-Desmorat (GD) model, Meissner-Matějka model, and bootstrapped eight-chain model predict well the three deformation modes compare to the classical eight-chain model

Using Machine Learning for Land Suitability Classification

Artificial intelligence and machine learning methods can be used to automate the land suitability classification. Multiple Classifier System (MCS) or ensemble methods are rapidly growing and receiving a lot of attention and proved to be more accurate and robust than an excellent single classifier in many fields. In this study a dataset based land suitability classification is addressed. It is done using a newly proposed ensemble classifier generation technique referred to as RotBoost, which is constructed by combining Rotation Forest and AdaBoost, and it is known to be the first time that RotBoost has been applied for suitability classification. The experiments conducted with the study area, Shavur plain, lies in the northern of Khuzestan province, southwest of Iran. It should be noted that suitability classes for the input data were calculated according to FAO method. This provides positive evidence for the utility of machine learning methods in land suitability classification especially MCS methods. The results demonstrate that RotBoost can generate ensemble classifiers with significantly higher prediction accuracy than either Rotation Forest or AdaBoost, which is about 99% and 88.5%, using two different performance evaluation measures

Emerging advances of nanotechnology in drug and vaccine delivery against viral associated respiratory infectious diseases (VARID)

Viral-associated respiratory infectious diseases are one of the most prominent subsets of respiratory failures, known as viral respiratory infections (VRI). VRIs are proceeded by an infection caused by viruses infecting the respiratory system. For the past 100 years, viral associated respiratory epidemics have been the most common cause of infectious disease worldwide. Due to several drawbacks of the current anti-viral treatments, such as drug resistance generation and non-targeting of viral proteins, the development of novel nanotherapeutic or nano-vaccine strategies can be considered essential. Due to their specific physical and biological properties, nanoparticles hold promising opportunities for both anti-viral treatments and vaccines against viral infections. Besides the specific physiological properties of the respiratory system, there is a significant demand for utilizing nano-designs in the production of vaccines or antiviral agents for airway-localized administration. SARS-CoV-2, as an immediate example of respiratory viruses, is an enveloped, positive-sense, single-stranded RNA virus belonging to the coronaviridae family. COVID-19 can lead to acute respiratory distress syndrome, similarly to other members of the coronaviridae. Hence, reviewing the current and past emerging nanotechnology-based medications on similar respiratory viral diseases can identify pathways towards generating novel SARS-CoV-2 nanotherapeutics and/or nano-vaccines. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

The ER Stress/UPR axis in chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Cellular protein homeostasis in the lungs is constantly disrupted by recurrent exposure to various external and internal stressors, which may cause considerable protein secretion pressure on the endoplasmic reticulum (ER), resulting in the survival and differentiation of these cell types to meet the increased functional demands. Cells are able to induce a highly conserved adaptive mechanism, known as the unfolded protein response (UPR), to manage such stresses. UPR dysregulation and ER stress are involved in numerous human illnesses, such as metabolic syndrome, fibrotic diseases, and neurodegeneration, and cancer. Therefore, effective and specific compounds targeting the UPR pathway are being considered as potential therapies. This review focuses on the impact of both external and internal stressors on the ER in idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) and discusses the role of the UPR signaling pathway activation in the control of cellular damage and specifically highlights the potential involvement of non-coding RNAs in COPD. Summaries of pathogenic mechanisms associated with the ER stress/UPR axis contributing to IPF and COPD, and promising pharmacological intervention strategies, are also presented

MicroRNA-211 Expression Promotes Colorectal Cancer Cell Growth In Vitro and In Vivo by Targeting Tumor Suppressor CHD5

Background: Chromodomain-helicase-DNA-binding protein 5 (CHD5) is a newly identified tumor suppressor that is frequently downregulated in a variety of human cancers. Our previous work revealed that the low expression of CHD5 in colorectal cancer is correlated with CHD5 promoter CpG island hypermethylation. In this study, we investigated the effect of microRNA-211 (miR-211)-regulated CHD5 expression on colorectal tumorigenesis. Methodology/Principal Findings: miR-211 was predicted to target CHD5 by TargetScan software analysis. A stably expressing exogenous miR-211 colorectal cancer cell line (HCT-116 miR-211) was generated using lentiviral transduction and used as a model for in vitro and in vivo studies. The expression level of miR-211 in HCT-116 miR-211 cells was upregulated by 16-fold compared to vector control cells (HCT-116 vector). Exogenous miR-211 directly binds to the 39-untranslated region (39-UTR) of CHD5 mRNA, resulting in a 50 % decrease in CHD5 protein level in HCT-116 miR-211 cells. The levels of cell proliferation, tumor growth, and cell migration of HCT-116 miR-211 cells were significantly higher than HCT-116 vector cells under both in vitro and in vivo conditions, as determined using the methods of MTT, colony formation, flow cytometry, scratch assay, and tumor xenografts, respectively. In addition, we found that enforced expression of miR-211 in HCT-116 cells was able to alter p53 pathway-associated regulatory proteins, such as MDM2, Bcl-2, Bcl-xL, and Bax. Conclusion/Significance: Our results demonstrate that CHD5 is a direct target of miR-211 regulation. Enforced expression o