Abstract

Over decades, the theoretical and applied mechanics community has developed sophisticated approaches for analysing the behaviour of complex engineering systems. Most of these approaches have targeted systems in the transportation, materials, defence and energy industries. Applying and further developing engineering approaches for understanding, predicting and modulating the response of complicated biomedical processes not only holds great promise in meeting societal needs, but also poses serious challenges. This report, prepared for the US National Committee on Theoretical and Applied Mechanics, aims to identify the most pressing challenges in biological sciences and medicine that can be tackled within the broad field of mechanics. This echoes and complements a number of national and international initiatives aiming at fostering interdisciplinary biomedical research. This report also comments on cultural/educational challenges. Specifically, this report focuses on three major thrusts in which we believe mechanics has and will continue to have a substantial impact. (i) Rationally engineering injectable nano/microdevices for imaging and therapy of disease. Within this context, we discuss nanoparticle carrier design, vascular transport and adhesion, endocytosis and tumour growth in response to therapy, as well as uncertainty quantification techniques to better connect models and experiments. (ii) Design of biomedical devices, including point-of-care diagnostic systems, model organ and multi-organ microdevices, and pulsatile ventricular assistant devices. (iii) Mechanics of cellular processes, including mechanosensing and mechanotransduction, improved characterization of cellular constitutive behaviour, and microfluidic systems for single-cell studies. In this scientific report, a for structural proteome-wide multi-targeted ligand-binding conserved binding pharmacophoric site Quantum Attack Resistent Certificateless Multi Receiver Signcryption Scheme comparison has been generated with USNCTAM perspectives on mechanics in Telomerase Peptide Vaccination simulated poly-chemo mimotopic pharmacological structure as a novel in silico promising anti-cancer drug-like agent in Stage IV Melanoma Patients.

Keywords

USNCTAM perspectives; on mechanics in medicine; Quantum Attack Resistent Certificateless; Multi Receiver; Signcryption Scheme; Telomerase Peptide Vaccination; simulated poly-chemo mimotopic; pharmacological structure; novel; in silico; promising anti-cancer drug-like agent; Stage IV Melanoma Patients; structural proteome-wide multi-targeted; ligand-binding conserved; binding pharmacophoric site comparison, nanoparticle-mediated; drug delivery, biomedical device design, cell mechanics.

Abstract

We propose a method to construct universal order parameters for quantum phase transitions in many-body lattice systems. The method exploits the H-orthogonality of a few near-degenerate lowest states of the Hamiltonian describing a given finite-size system, which makes it possible to perform finite-size scaling and take full advantage of currently available numerical algorithms. An explicit connection is established between the fidelity per site between two H-orthogonal states and the energy gap between the ground state and low-lying excited states in the finite-size system. The physical information encoded in this gap arising from finite-size fluctuations clarifies the origin of the universal order parameter. We demonstrate the procedure for the one-dimensional quantum formulation of the q-state Potts model, for q = 2, 3, 4 and 5, as prototypical examples, using finite-size data obtained from the density matrix renormalization group algorithm. Order parameters are pivotal to the Landau-Ginzburg-Wilson description of phase transitions for a wide range of critical phenomena, both classical and quantum, in many-body systems arising from spontaneous symmetry breaking (SSB)1,2. Despite their importance, relatively few systematic methods for determining order parameters have been proposed. One method proposed for quantum many-body lattice systems utilizes reduced density matrices3. This approach takes advantage of the degenerate ground states which appear when a symmetry of the Hamiltonian is broken spontaneously in the thermodynamic limit. An order parameter can be identified with an operator that distinguishes the degenerate ground states. The idea of the method is to search for such an operator by comparing the reduced density matrices of the degenerate ground states for various subareas of the system. This method was demonstrated in models that are considered to exhibit dimer, scalar chiral, and topological Universal Order substitution Parameters and Quantum Phase Transitions Finite-Size Approaches for high-resolution refinement and binding affinity estimated inhibitors targeted to the conserved CGQMCTVWCSSGC peptide mimetic pharmaco-structures with antagonizing VEGFR-3-mediated oncogenic effects.

Keywords

Universal Order Parameters, Quantum Phase Transitions Finite-Size Approaches, high-resolution refinement, binding affinity, estimation inhibitors, targeted conserved peptide, substitution mimetic pharmaco-structures, VEGFR-3-mediated oncogenic effects.

Abstract

We study the relation between hidden variables theories and quantum computation. We discuss an inconsistency between a hidden variables theory and controllability of quantum computation. To derive the inconsistency, we use the maximum value of the square of an expected value. We propose a solution of the problem by using new hidden variables theory. Also we discuss an inconsistency between hidden variables theories and the double-slit experiment as the most basic experiment in quantum mechanics. This experiment can be an easy detector to Pauli observable. We cannot accept hidden variables theories to simulate the double-slit experiment in a specific case. Hidden variables theories may not depicture quantum detector. This is a quantum measurement theoretical profound problem whether Hidden Variables Theories can meet Quantum Computation for high-resolution refinement and binding affinity estimation of inhibitors of CGQMCTVWCSSGC targeted conserved peptide substitution on mimetic pharmaco-structures with antagonizing VEGFR-3-mediated oncogenic effects?

Keywords

Hidden Variables Theories; Quantum Computation; high-resolution refinement; binding affinity estimations; inhibitors; CGQMCTVWCSSGC targeted; conserved peptide; substitution mimetic; pharmaco-structures; antagonizing VEGFR-3-mediated; oncogenic effects, Quantum Computer, Quantum Information Theory, Quantum Non Locality, Subject Areas: Applied Physics.

Abstract

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. The effectivenes of cancer vaccines in inducing CD8+Tcell responses remains a challenge, resulting in a need for testing more potent adjuvants. In previous clinical trials it has been determined the safetyand immunogenicity of vaccination against melanoma-related antigens employing MART-1,gp100, and tysosinase paptides combined with the TLR-9 agonist PF-3512676 and local GM-CSFin-oil emulsion.Using continuous monitoring of safety and a two-stage design for immunological efficacy, More than 20 immune-response evaluable patients were targetted. Vaccinations were given subcutaneously ondays 1 and 15 per cycle (1 cycle=28 days) for up to 13 cycles. Structure-based virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. That said, virtual screening is heavily dependent on detailed understanding of the tertiary or quaternary structure of the protein target of interest, including knowledge of the relevant binding pocket. Here, in Biogenea we have for the first time discovered a Computer-aided new cluster Simulation of algorithms and a Ligand-Based Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine on Glioma Growth Morphology generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma.

Keywords

Computer Simulation, Glioma Growth, Morphology Computer designed, Safe, immunogenic, pharmacophoric activator, mimicking physicochemical properties, MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D) inadjuvant, PF-3512676 and GM-CSF, clinical outcome, metastatic melanoma, new cluster, algorithms, Ligand-Based Virtual Screening approach, Support Vector, Information Fusion Bayesian Machine, Computer Simulation of Glioma Growth and Morphology; glioma, glioblastoma, computer simulation, 3-D, tumor growth, tumor morphology, mathematical model, cancer model.

Abstract

A phenomenological Hamiltonian giving the equation of motion of a non relativistic charges that accelerates and radiates is quantized. To derive the decay time the physical parameters entering the calculations are obtained from the theory of the hydrogen atom; the agreement of the predicted value with the experiments is striking although the mathematics is very simple. The theory is applied to the harmonic Quantum oscillator of a Radiating Harmonically Multidimensional Scaling of a SMAR1-derived engineered P44 cyclotidomimic agonisitic novel chemo-hyperstructure as a dual targeted mechanistic pharmacoligand for the Stochastic Resonance and Synergetic activation of the NF-κB pathways and p53 tumor suppressor pathways.

Keywords

Quantum Theory; Radiating Harmonically; Stochastic Resonance; Synergetics― Quantum Information Theory; Multidimensional Scaling; SMAR1-derived ;P44 peptide; tumor suppressor; p53.novel; chemo-hyperstructure; novel drug discovery; dual targeted; op53 and NF-κB pathways; p53 tumor suppressor pathway; engineered P44; cyclotidomimic; agonisitic; mechanistic pharmacoligand, Radiation Damping, Quantum Radiation, Phenomenological Hamiltonian

Abstract

A quantum information theory is derived for multidimensional signals scaling. Dynamical data modeling methodology is described for decomposing a signal in a coupled structure of binding synergies, in scale-space. Mass conservation principle, along with a generalized uncertainty relation, and the scale-space wave propagation lead to a polynomial decomposition of information. Statistical map of data, through dynamical cascades, gives an effective way of coding and assessing its control structure. Using a multi-scale approach, the scale-space wave information propagation is utilized in computing stochastic resonance synergies (SRS), and a data ensemble is conceptualized within an atomic structure. In this paper, we show the analysis of multidimensional data scatter, exhibiting a point scaling property. We discuss applications in image processing, as well as, in neuroimaging. Functional neuro-cortical mapping by multidimensional scaling is explained for two behaviorally correlated auditory experiments, whose BOLD signals are recorded by fMRI. The point scaling property of the information flow between the signals recorded in those two experiments is analyzed in conjunction with the cortical feature detector findings and the auditory tonotopic map. The brain wave nucleons from an EEG scan, along with a distance measure of synchronicity of the brain wave patterns, are also explained.

Keywords

Evaluation, Inverse Molecular Design Algorithm, Model Binding Site, In silico predicted, computer-aided molecular designed CTLA-4 blockador, increasement, antigen-specific CD8+ T-cells, inprevaccinated patients, melanoma, new cluster, algorithms, Large-Scale Protein-Ligand Docking experiment, inverse design, scoring function, protein-ligand interaction, cytochrome c peroxidase, dead-end elimination, drug design

Abstract

An increasing number of studies are aimed at modeling cellular environments in a comprehensive and realistic fashion. A major challenge in these efforts is how to bridge spatial and temporal scales over many orders of magnitude. Furthermore, there are additional challenges in integrating different aspects ranging from questions about biomolecular stability in crowded environments to the description of reactive processes on cellular scales. In this review, recent studies with models of biomolecules in cellular environments at different levels of detail are discussed in terms of their strengths and weaknesses. In particular, atomistic models, implicit representations of cellular environments, coarse-grained and spheroidal models of biomolecules, as well as the inclusion of reactive processes via reaction-diffusion models are described. Furthermore, strategies for integrating the different models into a comprehensive description of reaching new levels of realism in Modeling Biological hypermolecules on Glioma Growth Morphology generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma are discussed.

Keywords

Reaching New Levels; Realism in Modeling; Biological hypermolecules; Glioma Growth; Morphology generation; MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D); mimicking activator; PF-3512676; GM-CSF; clinical outcome; metastatic melanoma.

Abstract

The use of pharmacologically active short peptide sequences is a better option in cancer therapeutics than the full-length protein. Here we report one such 44-mer peptide sequence of SMAR1 (TAT-SMAR1 wild type, P44) that retains the tumor suppressor activity of the full-length protein. The protein transduction domain of human immunodeficiency virus, type 1, Tat protein was used here to deliver the 33-mer peptide of SMAR1into the cells. P44 peptide could efficiently activate p53 by mediating its phosphorylation at serine 15, resulting in the activation of p21 and in effect regulating cell cycle checkpoint. In vitro phosphorylation assays with point-mutated P44-derived peptides suggested that serine 347 of SMAR1 was indispensable for its activity and represented the substrate motif for the protein kinase C family of proteins. Using xenograft nude mice models, we further demonstrate that P44 was capable of inhibiting tumor growth by preventing cellular proliferation. P44 treatment to tumor-bearing mice prevented the formation of poorly organized tumor vasculature and an increase in hypoxia-inducible factor-1alpha expression, both being signatures of tumor progression. The chimeric TAT-SMAR1-derived peptide, P44, thus has a strong therapeutic potential as an anticancer drug.Abstract: The p53 and nuclear factor κB (NF-κB) pathways play crucial roles in human cancer development. Simultaneous targeting of both pathways is an attractive therapeutic strategy against cancer. The use of pharmacologically active short peptide sequences has prooven to be a better option in cancer therapeutics than the full-lengthprotein. It has been previously report ed one such 44-mer peptide sequence of SMAR1 (TAT-SMAR1 wild type, P44) that retains the tumor suppressor activity of the full-length protein.P44 peptide could efficiently activate p53 by mediating its phosphorylation at serine15, resulting in the activation of p21 and in effect regulating cell cycle checkpoint. In vitrophosphorylation assays with point-mutated P44-derived pep-tides suggested that serine 347 of SMAR1 was indispensable forits activity and represented the substrate motif for the proteinkinase C family of proteins. In this Research Scientific Project we generated Molecular dynamics and mechanistic in silico discovery simulations of a novel chemo-SMAR1-engineered p53 and NF-κB derived P44 cyclotidomimic agonisitic pharmacoligand P44 dual targeting hyperstructure for the activation of the p53 tumor suppressor pathway.

Keywords

Molecular dynamics simulations, in silico discovery, novel chemo-hyperstructure, drug discovery, dual targeting, NF-κB, pathways, activation, p53 tumor suppressor pathway, engineered, P44 cyclotidomimic, agonisitic, mechanistic, pharmacoligand, Molecular dynamics simulations and drug discovery, Keywords: molecular dynamics simulations, computer-aided drug discovery, cryptic binding sites, allosteric binding sites, virtual screening, free-energy prediction

Abstract

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.Computer designed of a Safe and immunogenic pharmacophoric activator mimicking physicochemical properties of the MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) inadjuvantwith PF-3512676 and GM-CSF with promising clinical outcome in metastatic melanoma using a new cluster of algorithms and a Ligand-Based Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine.

Keywords

Towards Structural Systems, Pharmacology Study, Complex Diseases, Personalized Medicine, Computer designed, immunogenic, pharmacophoric, activator, mimicking, physicochemical, properties, MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D), adjuvant, PF-3512676, GM-CSF, promising, clinical outcome, metastatic melanoma, new cluster of algorithms. Ligand-Based, Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine.

Abstract

The extraction of information from a quantum system unavoidably implies a modification of the measured system itself. In this framework partial measurements can be carried out in order to extract only a portion of the information encoded in a quantum system, at the cost of inducing a limited amount of disturbance. Here we analyze experimentally the dynamics of sequential partial measurements carried out on a quantum system, focusing on the trade-off between the maximal information extractable and the disturbance. In particular we implement two sequential measurements observing that, by exploiting an adaptive strategy, is possible to find an optimal trade-off between the two quantities for the testing of sequential quantum measurements on Glioma Growth Morphology for the generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma.

Keywords

Testing sequential; quantum measurements; maximal knowledge; Glioma Growth; Morphology; MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase; (368-376, 370D); mimicking activator; PF-3512676; GM-CSF; clinical outcome; metastatic melanoma;