PARALLEL OPTIMIZATION TECHNOLOGY FOR ATOMIC-MOLECULAR MORSE CLUSTERS

В статье рассматриваются специализированные вычислительные технологии и алгоритмы, используемые для поиска низкопотенциальных атомно-молекулярных кластеров. Проведенные вычислительные эксперименты продемонстрировали достаточно высокую конкурентоспособность новых алгоритмов по сравнению с классическими для функций рассматриваемого типа. С использованием разработанного программного комплекса получены рекордные результаты оптимизации атомно-молекулярных кластеров Морса рекордных размерностей. The paper deals with specialized computing technology and algorithms used for finding low-potential atomic-molecular clusters. The performed computational experiments demonstrated a rather high competitiveness of the new algorithms in comparison with the classical methods for the considerable functions. Using the developed software, the applied problem of molecular docking was solved. Using the developed software package, record results for optimization of atomic-molecular Morse clusters of large dimensions have been obtained.

Average density of states of amorphous Hamiltonians: role of phonon mediated coupling of nano-clusters

Based on a description of an amorphous solid as a collection of coupled nanosize molecular clusters referred as basic blocks, we analyse the statistical properties of its Hamiltonian. The information is then used to derive the ensemble averaged density of the vibrational states (nonphonon) which turns out to be a Gaussian in the bulk of the spectrum and an Airy function in the low frequency regime. A comparison with experimental data for five glasses confirms validity of our theoretical predictions.

A Novel Ferroptosis-Based Molecular Signature Associated with Biochemical Recurrence-Free Survival and Tumor Immune Microenvironment of Prostate Cancer

Objective: To identify ferroptosis-related molecular clusters, and to develop and validate a ferroptosis-based molecular signature for predicting biochemical recurrence-free survival (BCRFS) and tumor immune microenvironment of prostate cancer (PCa).Materials and Methods: The clinical data and transcriptome data of PCa were downloaded from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained from FerrDb database. We performed consensus clustering analysis to identify ferroptosis-related molecular subtypes for PCa. Univariate and multivariate Cox regression analysis were used to establish a ferroptosis-based signature for predicting BCRFS. Internal verification, external verification and subgroup survival analysis were then successfully performed.Results: There was a total of 40 differentially expressed FRGs in PCa. We then identified three ferroptosis-related molecular clusters of PCa, which have significantly different immune infiltrating cells, tumor immune microenvironment and PD-L1 expression level. More importantly, a novel ferroptosis-based signature for predicting BCRFS of PCa based on four FRGs (including ASNS, GPT2, NFE2L2, RRM2) was developed. Internal and external verifications were then successfully performed. Patients with high-risk score were associated with significant poor BCRFS compared with those with low-risk score in training cohort, testing cohort and validating cohort, respectively. The area under time-dependent Receiver Operating Characteristic (ROC) curve were 0.755, 0.705 and 0.726 in training cohort, testing cohort and validating cohort, respectively, indicating the great performance of this signature. Independent prognostic analysis indicated that this signature was an independent predictor for BCRFS of PCa. Subgroup analysis revealed that this signature was particularly suitable for younger or stage T III-IV or stage N0 or cluster 1-2 PCa patients. Patients with high-risk score have significantly different tumor immune microenvironment in comparison with those with low-risk score. The results of qRT-PCR successfully verified the mRNA expression levels of ASNS, GPT2, RRM2 and NFE2L2 in DU-145 and RWPE-1 cells while the results of IHC staining exactly verified the relative protein expression levels of ASNS, GPT2, RRM2 and NFE2L2 between PCa and BPH tissues.Conclusions: This study successfully identified three ferroptosis-related molecular clusters. Besides, we developed and validated a novel ferroptosis-based molecular signature, which performed well in predicting BCRFS and tumor immune microenvironment of PCa.

Single-Molecule Clustering for Super-Resolution Optical Fluorescence Microscopy

Molecular assembly in a complex cellular environment is vital for understanding underlying biological mechanisms. Biophysical parameters (such as single-molecule cluster density, cluster-area, pairwise distance, and number of molecules per cluster) related to molecular clusters directly associate with the physiological state (healthy/diseased) of a cell. Using super-resolution imaging along with powerful clustering methods (K-means, Gaussian mixture, and point clustering), we estimated these critical biophysical parameters associated with dense and sparse molecular clusters. We investigated Hemaglutinin (HA) molecules in an Influenza type A disease model. Subsequently, clustering parameters were estimated for transfected NIH3T3 cells. Investigations on test sample (randomly generated clusters) and NIH3T3 cells (expressing Dendra2-Hemaglutinin (Dendra2-HA) photoactivable molecules) show a significant disparity among the existing clustering techniques. It is observed that a single method is inadequate for estimating all relevant biophysical parameters accurately. Thus, a multimodel approach is necessary in order to characterize molecular clusters and determine critical parameters. The proposed study involving optical system development, photoactivable sample synthesis, and advanced clustering methods may facilitate a better understanding of single molecular clusters. Potential applications are in the emerging field of cell biology, biophysics, and fluorescence imaging.

CD8+ T Cell-Based Molecular Classification With Heterogeneous Immunogenomic Landscapes and Clinical Significance of Clear Cell Renal Cell Carcinoma

The tumor microenvironment (TME) exerts a high impact on tumor biology and immunotherapy. The heterogeneous phenotypes and the clinical significance of CD8+ T cells in TME have not been fully elucidated. Here, a comprehensive immunogenomic analysis based on multi-omics data was performed to investigate the clinical significance and tumor heterogeneity between CD8+ T cell-related molecular clusters. We identified two distinct molecular clusters of ccRCC (C1 and C2) in TCGA and validated in E-MTAB-1980 cohorts. The C1 cluster was characterized by unfavorable prognosis, increased expression levels of CD8+ T cell exhaustion markers, high immune infiltration levels as well as more immune escape mechanisms. The C2 cluster was featured by favorable prognosis, elevated expression levels of CD8+ T cell effector markers, low load of copy number loss and low frequency of 9p21.3 deletion. Moreover, the effect of molecular classifications on Nivolumab therapeutic efficacy in the CheckMate 025 cohort was examined, and the C2 cluster exhibited a better prognosis. Taken together, we determine two CD8+ T cell-related molecular clusters in ccRCC, and provide new insights for evaluating the functions of CD8+ T cells. Our molecular classification is a potential strategy for prognostic prediction and immunotherapeutic guidance for ccRCC patients.

Mini Review: Quantum Confinement of Atomic and Molecular Clusters in Carbon Nanotubes

We overview our recent developments on a computational approach addressing quantum confinement of light atomic and molecular clusters (made of atomic helium and molecular hydrogen) in carbon nanotubes. We outline a multi-scale first-principles approach, based on density functional theory (DFT)-based symmetry-adapted perturbation theory, allowing an accurate characterization of the dispersion-dominated particle–nanotube interaction. Next, we describe a wave-function-based method, allowing rigorous fully coupled quantum calculations of the pseudo-nuclear bound states. The approach is illustrated by showing the transition from molecular aggregation to quasi-one-dimensional condensed matter systems of molecular deuterium and hydrogen as well as atomic 4He, as case studies. Finally, we present a perspective on future-oriented mixed approaches combining, e.g., orbital-free helium density functional theory (He-DFT), machine-learning parameterizations, with wave-function-based descriptions.