Deformation induced evolution of plasmonic responses in polymer grafted nanoparticle thin films.

Multi-functional nanoparticle thin films are being used in various applications ranging from biosensing to photo-voltaics. In this study, we integrate two different numerical approaches to understand the interplay between the mechanical deformation and optical response of polymer grafted plasmonic nanoparticle (PGPN) arrays. Using numerical simulations we examine the deformation of thin films formed by end-functionalised polymer grafted nanoparticles subject to uniaxial elongation. The induced deformation causes the particles in the thin film network to rearrange their positions by two different mechanisms viz. sliding and packing. In sliding, the particles move in the direction of induced deformation. On the other hand, in packing, the particles move in a direction normal to that of the induced deformation. By employing a Green's tensor formulation in polarizable backgrounds for evaluating the optical response of the nanoparticle network, we calculate the evolution of the plasmonic response of the structure as a function of strain. The results indicate that the evolution of plasmonic response closely follows the deformation. In particular, we show that the onset of relative electric field enhancement of the optical response occurs when there is significant rearrangement of the constituent PGPNs in the array. Furthermore, we show that depending on the local packing/sliding and the polarization of the incident light there can be both enhancement and suppression of the SERS response.

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.

Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

Mechanical response of networks formed by end-functionalised spherical polymer grafted nanoparticles.

Via computer simulations we examine the mechanical response of hybrid polymer-particle networks composed of rigid spherical nanoparticles with long flexible polymer chains grafted onto their surface. The canopy of grafted polymer arms are end-functionalised such that interacting polymer-grafted nanoparticles (PGNs) form labile bonds when their coronas overlap. In the present study, the number of grafted arms, f, are such that the PGN brushes are in the small (f = 600) and intermediate curvature (f = 900 and 1200) regime with stable bonded interactions. To investigate the mechanical response of networks formed by these PGNs, controlled uniaxial elongation at a specified pulling rate is imposed on a 2-D network of PGNs placed on a hexagonal lattice. In the simulations, the force required to deform the network is measured as a function of the elongation and pulling rate imposed on the network until the network fails. By analysis of the force-strain curves and the rearrangement of the PGNs in the network we show that an increase in the number of grafted arms, pulling velocity and energy of the bonded interactions alters both the toughness and the mode of failure of the networks. In particular, we show that an increase in the number of grafted arms results in a reduction of toughness. Furthermore, analysis of the simulations of force relaxation after rapid extension indicates that the relaxation in deformed networks can be characterised by one or two time scales that depend on the number of grafted arms. The analysis of force-strain curves and force relaxation demonstrate the role of Deborah number, De, and the limitations in the use of a unique De in understanding the mechanical response of the networks respectively.

LungMAP Portal Ecosystem: Systems-Level Exploration of the Lung.

An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development, and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, re-analysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). As this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP and external research efforts.

Effect of functional anisotropy on the local dynamics of polymer grafted nanoparticles.

End-functionalised polymer grafted nanoparticles (PGNs) form bonds when their coronas overlap. The bonded interactions between the overlapping PGNs depend on the energy of the bonds (U). In the present study, oscillatory deformation imposed on a simple system with interacting PGNs placed on the vertices of a triangle is employed to examine the local dynamics as a function of energy of the bonds and the frequency of oscillation relative to the characteristic rupture frequency, ω0 = 2πν exp(-U/kBT), of the bonds. In particular, the effect of functional anisotropy is studied by introducing bonds of two different energies between adjacent PGNs. A multicomponent model developed by Kadre and Iyer, Macromol. Theory Simul., 2021, 30, 2100005, that combines the features of effective interactions between PGNs, self-consistent field theory and master equation approach to study bond kinetics is employed to obtain the local dynamics. The resulting force-strain curves are found to exhibit a simple broken symmetry where Fx (γ,) ≠ -Fx (-γ,-) and Fy (γ,) ≠ Fy (-γ,-) in systems with functional anisotropy. Fourier analysis of the dynamic response reveals that functional anisotropy leads to finite even harmonic terms and systematic variation of both the elastic and dissipative response from that of the isotropic systems. Furthermore, the intra-cycle variations in the strain stiffening and shear thickening ratios obtained from the analysis indicate that functional anisotropy leads to anisotropic nonlinear response.

DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity.

Cytolytic T-cells play an essential role in the adaptive immune system by seeking out, binding and killing cells that present foreign antigens on their surface. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life-threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native peptides to elicit a T-cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen alleles, for both synthetic biological applications, and to augment real training datasets. Here, we propose a beta-binomial distribution approach to derive peptide immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, K-nearest neighbors, support vector machine, Random Forest and AdaBoost) and three deep learning models (convolutional neural network (CNN), Residual Net and graph neural network) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-CoV-2). We chose the CNN as the best prediction model, based on its adaptivity for small and large datasets and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepImmuno-CNN correctly predicts which residues are most important for T-cell antigen recognition and predicts novel impacts of SARS-CoV-2 variants. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physicochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface.

DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity.

T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native epitopes to elicit a T cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen (HLA) alleles, for both synthetic biological applications and to augment real training datasets. Here, we proposed a beta-binomial distribution approach to derive epitope immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, KNN, SVM, Random Forest, AdaBoost) and three deep learning models (CNN, ResNet, GNN) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-Cov-2). We chose the CNN model as the best prediction model based on its adaptivity for small and large datasets, and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepHLApan and IEDB, DeepImmuno-CNN further correctly predicts which residues are most important for T cell antigen recognition. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physiochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface. DATA AVAILABILITY: DeepImmuno Python3 code is available at https://github.com/frankligy/DeepImmuno . The DeepImmuno web portal is available from https://deepimmuno.herokuapp.com . The data in this article is available in GitHub and supplementary materials.

The Impact of Heartfulness-based Elective on Middle School Students.

Objectives: There is an increasing trend in the levels of stress, anxiety, and depression among adolescents aged 12 to 17. The Heartfulness Program for Schools (HPS) is a program designed to manage stress and build social-emotional skills to cope with real-life challenges. This quantitative study explores the impact of HPS customized for middle school students. Methods: Participants recruited from the 7th and 8th grade classrooms, including the HPS group (N = 74) and control group (N = 38), completed 2 surveys that measured levels of stress and well-being at baseline and after the completion of the 13-week elective. Results: Data collected from the pre-test resulted in similar baseline scores for both groups. Post-test findings revealed a statistically significant decrease in the stress levels in the HPS group showing improvement in coping skills, stress management, and increase in overall well-being. Conclusions: This study suggests that HPS helps reduce stress levels and improve well-being in children by cultivating positivity and fostering social and self-awareness. Integrating HPS in a school curriculum will benefit the students in building their emotional intelligence, and will nurture their relationship and mental well-being within and beyond the school.

Designing mechanomutable composites: reconfiguring the structure of nanoparticle networks through mechanical deformation.

Via a new dynamic, three-dimensional computer model, we simulate the tensile deformation of polymer-grafted nanoparticles that are cross-linked by labile bonds, which can readily rupture and reform. For a range of relatively high strains, the network does not fail, but rather restructures into a stable, ordered structure. Within this network, the reshuffling of the labile bonds enables the formation of this new morphology. The results provide guidelines for designing mechano-responsive hybrid materials that undergo controllable structural transitions through the application of applied forces.

Modeling polymer grafted nanoparticle networks reinforced by high-strength chains.

Using a multi-scale computational approach, we determine the effect of introducing a small fraction of high-strength connections between cross-linked nanoparticles. The nanoparticles' rigid cores are decorated with a corona of grafted polymers, which contain reactive functional groups at the chain ends. With the overlap of neighboring coronas, these reactive groups can form weak labile bonds, which can reform after breakage, or stronger bonds, which rupture irreversibly and thus, the nanoparticles are interconnected by dual cross-links. We show that this network can be reinforced by the addition of high-strength connections, which model polymer arms bound together by bonds with energies on the order of 100 kBT. We demonstrate that in the course of these simulations, these high-strength connections can be treated as unbreakable chains. By subjecting networks with a random distribution of the unbreakable chains to tensile deformation at a constant strain-rate, we determine the distribution of strain at break and toughness. With even a small amount of unbreakable chains, the nanoparticle networks can survive strains far greater than the networks without these connections. Furthermore, networks containing the high-strength connections tend to form long, thin threads, which enable a larger strain at break. The findings provide guidelines for creating polymer grafted nanoparticles networks that could show remarkable strength and ductility.

Lattice animal model of chromosome organization.

Polymer models tied together by constraints of looping and confinement have been used to explain many of the observed organizational characteristics of interphase chromosomes. Here we introduce a simple lattice animal representation of interphase chromosomes that combines the features of looping and confinement constraints into a single framework. We show through Monte Carlo simulations that this model qualitatively captures both the leveling off in the spatial distance between genomic markers observed in fluorescent in situ hybridization experiments and the inverse decay in the looping probability as a function of genomic separation observed in chromosome conformation capture experiments. The model also suggests that the collapsed state of chromosomes and their segregation into territories with distinct looping activities might be a natural consequence of confinement.

Hierarchies in eukaryotic genome organization: Insights from polymer theory and simulations.

Eukaryotic genomes possess an elaborate and dynamic higher-order structure within the limiting confines of the cell nucleus. Knowledge of the physical principles and the molecular machinery that govern the 3D organization of this structure and its regulation are key to understanding the relationship between genome structure and function. Elegant microscopy and chromosome conformation capture techniques supported by analysis based on polymer models are important steps in this direction. Here, we review results from these efforts and provide some additional insights that elucidate the relationship between structure and function at different hierarchical levels of genome organization.

Flexible ring polymers in an obstacle environment: Molecular theory of linear viscoelasticity.

We formulate a coarse-grained mean-field approach to study the dynamics of the flexible ring polymer in any given obstacle (gel or melt) environment. The similarity of the static structure of the ring polymer with that of the ideal randomly branched polymer is exploited in formulating the dynamical model using aspects of the pom-pom model for branched polymers. The topological constraints are handled via the tube model framework. Based on our formulation we obtain expressions for diffusion coefficient D, relaxation times tau, and dynamic structure factor g(k,t). Further, based on the framework we develop a molecular theory of linear viscoelasticity for ring polymers in a given obstacle environment and derive the expression for the relaxation modulus G(t). The predictions of the theoretical model are in agreement with previously proposed scaling arguments and in qualitative agreement with the available experimental results for the melt of rings.