However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven “defectometer” to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them.Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them,  Defects and disordered local domains in soft, self-assembled aggregates determine their dynamic and adaptive properties, and enable communication between entities, but characterizing and classifying such intricate dynamic behaviors is highly complex. Here, the authors report on a data-driven workflow to identify objective criteria for the comparison of complex dynamic features in soft supramolecular materials, deriving a data-driven ’defectometer’ that allows to classify soft self-assembled materials based on the structural dynamics of the ordered/disordered molecular environments that statistically emerge within them. Among many interesting features, the highest potential of soft supramolecular materials lies in their intrinsically dynamic character at room temperature5. The self-assembled monomers continuously exchange within and in-and-out these materials7, controlling how they communicate with each other at the equilibrium9, how they respond to external stimuli10, and how they behave out-of-equilibrium11. Such dynamical features determine a set of intriguing properties that, on the one hand are crucial for the functioning of biological tissues 16 and, on the other hand, are promising features for the design of new functional materials and nano-technologies23.Supramolecular structures, composed of molecular units that self-assemble 27. However, because of the complexity and the dynamical, multiform nature of such systems31, a direct connection between the collective properties of supramolecular materials and the microscopic features of the constituent monomers remains often impossible to attain.A major goal in the study of self-assembled architectures is understanding how changes in the structure of the self-assembling building blocks (input) affect the overall properties of the supramolecular assembled architecture (output) Fig. . Gaining36. However, analysing and interpreting the high-dimensional, high-detail data produced by computer simulations can be challenging, particularly for supramolecular systems. In such complex structures, irregularities and defects play a crucial role16, demanding for molecular descriptors capable to translate the simulation data into more reliable classifications of the molecular structure and dynamics characteristic of these systems.Molecular simulations play a fundamental role in this framework, providing abundant data on the structure and dynamics of supramolecular assemblies, with thorough chemical-physical information on the factors that control the dynamical features of these materials41. These techniques allow to fully exploit and analyse the rich, high-dimensional information contained in high-resolution MD simulations, providing insight on key patterns and correlations that may remain hidden to conventional human-based analyses44."Human-based” analyses rely on low-dimensional descriptors, defined based on the experience and on those features directly readable by visual inspection. This often leads to biased predictions, affected by the choice of such low-dimensional descriptors, which possibly overlook important degrees of freedom of the system. To overcome these limitations, data-driven, Machine-Learning (ML) approaches, such as, e.g., unsupervised clustering, dimensionality reduction, pattern recognition, etc., are particularly useful to characterize complex self-assembling systems49.In particular, data-driven analyses allow to effectively monitor the local environment of each atom/molecule in a system, providing accurate, high-dimensional atomic/molecular descriptors, called “fingerprints”, that classify the mutual arrangement of atomic/molecular entities 49, proved to be a very efficient density-based descriptor. It encodes the information of atomic environments into a rich, high-dimensional and agnostic  fingerprint52. Coupling a descriptor-based analysis with unsupervised clustering techniques enables a robust characterization of the ordered/disordered arrangement in molecular systems, as demonstrated for Lennard-Jones clusters, peptides51, and water55. Recently, combining SOAP descriptors and unsupervised clustering, we were able to reconstruct the complex structural dynamics of molecules in supramolecular materials, tracking e.g., defects formation and evolution7. This data-driven approach, in principle enables a rigorous and unbiased classification of soft structures57.The Smooth Overlap of Atomic Position (SOAP)In the present work we push the data-driven comparability between supramolecular assemblies to the limit. Based on SOAP description and unsupervised clustering, as well as on a SOAP-based metric, we construct a data-driven analysis strategy, which can be used as a “defectometer”, measuring and comparing different structures based on their local molecular environments. This allows us to compare different types of assemblies, such as supramolecular fibers, micelles, layers, nanoparticles, etc. We obtain a general, unbiased classification of supramolecular systems that are profoundly different among each other, capturing structural and dynamical aspects that are hardly readable with standard descriptors.7. In the following, we characterise the behavior of different types of soft, supramolecular self-assembled materials as emerging from equilibrium MD trajectories obtained using CG molecular models. For all considered systems, we compute the SOAP spectra associated to the local molecular environment that surrounds each monomer core in the system, at every sampled step of an equilibrium CG-MD trajectory. Gathering these SOAP spectra in global datasets, we can compare and classify different systems. (i) Combining dimensional reduction and unsupervised clustering techniques, we can determine from the SOAP dataset the main molecular motifs present in each studied assembly. This allows us, for example, to identify defects in such soft assemblies that are key for their intrinsic dynamics8. (ii) By defining a SOAP-based metric, we can compute the distance between the compared systems in the high-dimensional SOAP space, assessing and quantifying their similarity in terms of the SOAP monomeric motifs that emerge and are present within them, in equilibrium conditions56. This approach is first applied to a set of one-dimensional supramolecular polymers, having different monomer structures, and is then extended to compare and classify two-dimensional as well as three-dimensional assemblies with each other, proving a remarkable flexibility.The formation and dynamics of defects play a fundamental role in determining the properties of soft, supramolecular assemblies. Detecting and classifying such structural defects require adequate descriptors of local molecular environments, carrying high-dimensional information on the monomer arrangements and dynamicity. In this framework, general, data-driven descriptors and analyses were recently proven to be more effective than standard, “human-based” descriptors, especially for complex and dynamic molecular systems7. In particular, monitoring the SOAP vectors defined in the center of each monomer in a BTA supramolecular fiber allows us to retrieve detailed information on the structural order/disorder , as well as on the internal monomer dynamics that characterize such supramolecular polymers. A pre-requisite is that the MD simulation trajectories considered in the analysis provide sufficient sampling of the equilibrium of the simulated systems . All simulations used in the following analyses meet such requirement .Recently, it has been shown that unsupervised clustering of SOAP data from equilibrium MD of 1,3,5-Benzenetricarboxamides (BTA) supramolecular polymers allows the reconstruction of the structural dynamics of such complex assemblies7. In these analyses, the resolution of the employed molecular models is crucial, as it implicitly determines the accuracy by which, e.g., the monomer-monomer interactions, the monomers’ flexibility, etc., are described. Coarse-grained (CG) models with a resolution <5 Å— developed based on the Martini force-field58, and optimized to match the behavior of the respective all-atom (AA) models—were proven accurate enough to capture all essential information while guaranteeing sufficient sampling of the assembled fibers in equilibrium34. With such CG models, it was demonstrated that water-soluble BTA polymers, composed of BTAW monomers with amphiphilic arms, possess a variegated structure with a rich and diverse set of monomeric states, in continuous exchange and dynamic interconnection with each other. The SOAP data of the BTAW fiber were analysed via the Probabilistic Analysis of Molecular Motifs (PAMM) unsupervised clustering (see Methods section for details)51. As shown in Fig. W fiber  of monomers form defects that are continuously created-and-repaired 7.Considering different variants of BTA fibers, and combining the SOAP data obtained for each system, we can assemble a dataset gathering all possible configurations (each one identified by a SOAP feature vector) visited by monomers in the different BTA fiber variants. This guarantees that SOAP vectors in this dataset, associated to the local arrangement of monomers in all the different assembly variants, belong to a unique high-dimensional feature space, thus allowing us to compare between different systemsIt is worth noting that such a direct comparison between fiber variants is possible since all SOAP vectors, sampled in the compared systems, belong to the same high-dimensional feature space . This approach allows to identify defects  in the supramolecular fibers, to track their emergence/disappearance, and to use them to compare fiber variants. Moreover, as the concept of defects is elusive in such dynamical structures, this approach is general and flexible, as the definition of the molecular motifs that statistically populate the assemblies, and thus of defects, is exquisitely data-driven. This analysis can be thus extended to other families of assemblies. As a first proof-of-concept, we proceed by comparing variants of different supramolecular polymer families.59  produces instead a more disordered fiber, rich of defects . The ordered monomers in the BTTF5 fibers are only ~32% (~78% in BTTF), and considerable dynamic interconversions are observed between all monomeric states 59 Fig. A, B and  60 Fig. C, D. We ems Fig. A, B. Alsone Fig. A: in redcts Fig. C, D, sho8, the possibility to create and repair defects in the assembly is crucial for their dynamic properties10. The analyses of Figs. The proposed analysis allowed us to qualitatively detect the statistical formation of relevant molecular motifs such as defects. This is an important ingredient since, as demonstrated for BTA supramolecular polymers7. As detailed in the Methods section, we considered only the position of monomer centers in the SOAP calculation, with two advantages: (i) this has been shown to retain sufficient information on the monomer arrangement in the fibers, and thus on their supramolecular structure and dynamics8. (ii) This makes the analysis very general/abstract—all assemblies are composed of mutually interacting monomers—opening the possibility to compare, not only variants of a supramolecular fiber, but also widely different assemblies.As described above, by processing a comprehensive dataset containing SOAP vectors sampled via MD simulation of multiple systems, one can build a framework to rigorously and unambiguously compare different supramolecular systems. The idea is to retain in the SOAP analysis those relevant features that are common to all the systems (a sort of “common molecular denominator”)via the equilibrium MD of the systems simulated for the analyses of previous section , obtaining the global contour plot reported in Fig. To this end, we thus built a joint dataset containing all the SOAP vectors sampled 56, or liquid aqueous systems55, we build on such high-dimensional metric to quantify the similarity between the SOAP data associated to each fiber (see Methods for details). For each frame extracted from an equilibrated MD simulation, SOAP vectors associated to the local environments surrounding all monomers are averaged into a single SOAP spectrum  between the SOAP simulation-averages of the various systems. Such distance is defined directly in the SOAP space, and preserves the complete information contained in such high-dimensional descriptors. The result of this analysis is the dSOAP distance matrix in Fig. dSOAP matrix grant a classification of the assemblies under investigation in the global SOAP space. The darker the color of the entry, the lower is the dSOAP between the assemblies, indicating their similarity (in terms of molecular environments). The dSOAP matrix confirms the qualitative indications provided by the scatter plots of Fig. C8, BTA*, and NDIO, are similar and mainly populated by ordered, backbone-like molecular domains . Proven useful to compare complex interacting molecular systems, such as, e.g., lipid bilayers. 56 Eq.  to compuins Fig. A: in redins Fig. E. The reins Fig. , 2, are ons Fig. A, B. ThiClassifying different types of supramolecular polymers, these results demonstrate how the proposed approach is effective in comparing 1D assemblies in general. This is possible through the unbiased, high-dimensional, SOAP-based metric, that quantitatively compares the average molecular environment (and defects) emerging in the various assemblies. The flexibility of the approach suggests to investigate whether such a “defectometer” can be used to compare assemblies with higher structural dimensionality .We here extend our approach to compare 2D supramolecular systems. We focus on assemblies where the monomers may be arranged on flat, as well as on curved surfaces. As case studies, we chose DPPC lipids, which self-assemble into 2D (planar) lipid bilayers, and DPC and SDS surfactants, that form nearly spherical micellar aggregates. For all these systems, we employed validated Martini-based CG models (same resolution of the previously studied models) and collected equilibrium MD trajectories for the SOAP analysis Fig. A. Also i56. In our analysis, we considered equilibrium MD trajectories for DPPC at T = 273, T = 293, and T = 323 K, as representative of 2D planar assemblies having variable reconfiguration dynamics of monomers, depending on the bilayer gel/liquid state. Analysing the DPPC trajectories at different temperatures, we first performed a SOAP + PAMM analysis, analogous to the SOAP + PAMM comparisons between fiber variants belonging to the same family, in Figs. 56. When the temperature raises to ~323 K, the bilayer is entirely liquid, with residual (<5%), gel-like lipids  of two micellar aggregates, made of DPC or SDS surfactants. The PCA scatter plots . This suggests that the structural/dynamical features of the monomeric environments characterizing these micelles are closer to those of a dynamic/liquid, rather than of a static/gel-like flat bilayer.We thus, extended our SOAP + PAMM analysis to curved 2D assemblies, enriching the dataset with the SOAP data obtained from the equilibrium MD  to dynamic micelles formed by different monomers (SDS/DPC), than to the DPPC bilayer at lower temperature . Therefore, all systems contribute to the global SOAP dataset in equal percentages (see Methods for details).We tested this approach also on soft self-assembled 3D systems. In particular, we focused on spherical-shaped nanoparticles. As an example, we chose hexadecane (HEXA), an hydrophobic alkane composed of 16 Carbon atoms, for which we employed a Martini-based CG model. HEXA molecules undergo aggregation forming spherical assemblies (droplets) in water. We tested our method by comparing assemblies of variable size, composed of 128, 512 and 2048 molecules . An important parameter for the SOAP analysis is the cutoff radius (rcut), which determines the size of the neighborhood considered in characterizing the molecular environment of each SOAP center7. In principle, the dSOAP scale may change while changing the rcut in the analysis. In order to prove the robustness of our analysis, and given the differences in the assemblies compared herein, we computed the dSOAP matrix using three different rcut values: 0.8, 1.6, and 3.0 nm. Shown in Fig. dSOAP between the various systems when a shorter rcut is used . The bottom panels of Fig. dSOAP matrices of the top panels, with an adapted color scale. Aside from subtle differences—e.g., a slightly lower resolution when a smaller rcut is used—the global picture in terms of similarity between the compared assemblies is consistent in all cases , two neighboring/entangled ones for the flat and spherical 2D assemblies, and a third one for the 3D aggregates (bottom-right in the matrix). An interesting exception is the BTAW fiber, found more similar to 2D assemblies (DPPC and surfactant micelles) than to the other ordered 1D assemblies. Another interesting system is the DPPC bilayer at 323 K, which is found closer to highly dynamic SDS and DPC micelles rather than to DPPC at lower temperature (293 or 273 K).In general, in the dSOAP matrix, one can select one assembly and rank all the others with respect to it. For example, selecting the ordered BTAC8 fiber, the plot of Fig. dSOAP) with the other 1D ordered assemblies, lower similarity with disordered 1D fibers , while dSOAP increases further with respect to 2D and 3D assemblies. As anticipated above, an interesting result is obtained for BTAW 8 can shape a molecular environment in the surrounding of the monomers that is closer to the environment of 2D micelles or liquid-like lipid bilayers than to the environment of ordered BTA variants . Noteworthy, this is known to produce a dynamic surface adaptability in this specific fiber that is similar to the surface fluidity seen, e.g., in lipid bilayers10. The relative dSOAP distances in the matrix , to assess the similarity interconnections among all considered systems. This led to the global dendrogram shown in Fig. W fiber is, for example, closer to the ensemble of 2D assemblies—bilayers and micelles (highlighted in green) -, rather than to that of ordered 1D fibers (in blue)10. This demonstrates that the proposed SOAP-based analysis can provide a handle to classification exceeding the typical ones based on, e.g., the theoretical dimensionality of an assembly, monomer-monomer coordination number, etc., which cope badly with complex and dynamic assemblies. This crucial difference between “standard” human-based classifications and the data-driven method proposed herein is underlined further in Fig. dSOAP from the BTAC8 (C) and BTAW (D) systems, showing that a correlation between microscopic similarity and a priori theoretical dimensionality is not obvious when dealing with soft dynamic assemblies as those studied herein.In the It is worth noting that such similarity measurements rely solely on data extracted from equilibrium MD simulations, with no major assumption on the structure/features of the studied materials. While this approach is flexible, and can be used to compare in an unbiased way different types of materials, it also creates the important opportunity to classify assemblies based on the molecular environments that populate them, which is a crucial step towards the rational design of supramolecular materials with programmable dynamic properties.Rigorous and precise classification of the structure and dynamics of soft supramolecular materials is essential for the rational design of monomers that can self-assemble into supramolecular structures having controllable dynamic behavior. While it is known that the dynamics of such materials originates from defect states that statistically populate them, it is not trivial to design a unified, unbiased, and robust approach to classify soft assemblies based on their “defectivity”.via a synergistic use of atomic environment descriptors, unsupervised clustering, and similarity/dissimilarity measurements in a high-dimensional feature space. Monomers’ displacement/arrangement data extracted from the MD simulations are translated into relevant information on the molecular arrangements within the assemblies by means of SOAP feature vectors49. The high-dimensional data contained in the resulting SOAP spectra are then used to identify dominant monomer clusters/states in the different assemblies via unsupervised PAMM clustering (and PCA)51. The analysis outputs a characteristic fingerprint for each system in terms of the most populated molecular motifs, their structural and dynamical features. A metric defined in the high-dimensional SOAP feature space56 then provides a quantitative way to compare and classify the studied systems. We show how such data-driven analysis can be used to compare a wide range of supramolecular systems, providing a classification based on the monomeric motifs that emerge from equilibrium MD data, overcoming standard classification approaches based on a priori assumptions on the features of the assemblies . The obtained results also demonstrate how, monitoring the monomer transitions between the detected molecular motifs, it is possible to obtain relevant information on the inner dynamics of the assemblies. We can observe how ordered 1D stacked fibers are quite similar with each other, while defected supramolecular polymers  have a richer and diverse internal structure/dynamics, closer to that of some types of considered lipid bilayers or micelles. This fits well, e.g., with the complex dynamics of the surface of BTAW water-soluble fibers63, and with their dynamic adaptivity and structural reconfigurability 64.Here we report an unsupervised, machine-learning approach that allows comparison and classification of soft self-assembled materials  based on the structural dynamics of the internal molecular environments that surround their constituive monomers. We simulate these supramolecular assemblies via molecular models having submolecular (<5 Å) resolution, and analyse their equilibrium MD trajectories This workflow is powerful for multiple reasons. (i) It does not build on any a priori knowledge on the structure and dynamics of the various assemblies that are compared. (ii) Building on the concept of “defectivity”, it proposes defects as a common ground to compare different materials, a direction that holds a great potential to unify supramolecular materials. (iii) Such a data-driven “defectometer” allows to quantitatively classify dynamic assemblies that are different from each other . This provides us with a precious tool toward the rational design of self-assembled materials with controllable dynamic properties, which is key to conceive complex systems where multiple assembled entities can effectively communicate with each other in a dynamic way.R(t) of the molecular species in the system at timestep t. R is a 3N-dimensional configuration vector, where N is the number of particles. A generic descriptor is a mapping from the 3N-dimensional coordinate space to a D-dimensional feature space, that associates a feature vector to each of the sampled conformations R(t). We aim at a descriptor capable to characterize and compare the atomic/molecular environment surrounding the multiple constituents of a soft, supramolecular system. A first, crucial requirement of this mapping is that it preserves physical symmetries such as permutation, translation and rotation invariance, ensuring that physically equivalent configurations are recognized as such by the descriptor65. Other less obvious properties, required for a useful representation of the atomic/molecular environment are e.g., smoothness, additivity and level of locality; SOAP descriptors satisfy all these requirements 67.Let a generic output of an MD simulation be the ensemble of system conformations sampled at a series of MD timesteps, represented by the atomic coordinates 49 is an atom-centered descriptor, that accurately reproduces density correlation features of many-body systems. SOAP were introduced in ref. 49 as new bond-order parameters, able to efficiently account for radial and angular information of the environment that surrounds atoms or molecules66. The SOAP descriptor of a system of N particles describes the atomic/molecular surroundings of a selected set of M coordinates of the system components, which are referred to as the “centers” of the SOAP vector. These M centers can include the position of every single atom of the system, as well as selections or combinations  of them. In the following, we comment on the choice of the SOAP center for each of the studied systems, but in general we associate a single SOAP vector per each monomer . In this work we have considered only single-specie systems, but the approach is generalizable to multiple species.The SOAPj that surround the i-th center, namelyr = rij from the i-th center), to build a smooth density function. The σ parameter sets the width of the Gaussian located at the j-th neighboring center. The total neighbor density ρ = ∑iρi is retrieved by summing all contributions. The function frcut smoothly goes from 1 to 0 at rcut, so that the environment of each center extends up to a fixed cutoff rcut. Starting from Equation (Rn(r) and spherical harmonics 66.The SOAP descriptor is built from the density of neighboring centers ith-center. The full SOAP descriptor associated to the i-th center is a vector including all contributions from Eq. n and l ranges from 1 to D of the vector 49. To compute Eq. 68, using nmax, lmax = 8 and three different cutoff values rcut = 0.8, 1.6, 3.0 nm. The remaining parameters were set to default values of the DScribe library.We here employ the second-order SOAP descriptor, also called SOAP power spectrum , which can be written asN-dimensional configuration vector R sampled via CG-MD, we define a set of M centers {i}, and for each center we compute the SOAP vector {pi}. This generates a dataset of M SOAP vectors that describe the structural arrangement of the centers in the selected configuration of the system. Such SOAP vectors represent “local” descriptors, encoding the information on the environments that surrounds each center.To summarize, for a given 3frame-average SOAP descriptor M centers, namely 68:simulation-average SOAP descriptor,frame-average SOAP descriptors along the T frames collected through the MD simulation. Equation T frames are sampled at the equilibrium; we used the frame-average SOAP to assert similarities between different structures. See further details in Supplementary Figs. We can also define “global” descriptors, useful for the comparison of different systems: (i) the mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimvia a distance estimation (or kernel function), provided that the dimensionality D of the compared SOAP vectors is the same  between the compared systems. We measure the similarity between two SOAP vectors using a linear polynomial kernel 50,q = p/∣p∣ is the unit-normalized SOAP vector. Upon normalization, 6756 to compare and classify the structural arrangement of lipid membranes represented via different force-fields. We here apply the same approach for the comparison of different supramolecular systems, computing Eq. simulation-average SOAP vectors obtained via the simulation of each different test case.Once SOAP feature vectors are computed, the similarity between the SOAP characterization of different supramolecular structures can be inferred dSOAP matrices in Fig. dSOAP with respect to the results obtained with larger rcut . High-dimensional data, while rich in information, are not well-suited for visualization and classification/clustering, especially with methods based on Euclidean distance. To overcome these limitations a dimensionality reduction method is necessary.The SOAP data output of an MD simulation consists of a set of feature vectors Eq.  of dimen70, a widely used method that was already proven to handle with efficacy high-dimensional SOAP data obtained from high-resolution MD simulations of complex molecular and supramolecular systems56. For such systems, PCA was found to offer a good compromise between efficiency, reliability of the results and affordable computational cost via the python class sklearn.decomposition.PCA from the python library Scikit-learn71. After PCA, we chose to keep only three principal components, (if not stated otherwise), as this allows us to maintain more than 90% of the total variance in all our cases  of these dimensionally reduced SOAP vectors is estimated by means of a Kernel Density Estimation (KDE), built from a multivariate anisotropic Gaussian function in the 3D space. In order to mitigate the computational load of KDE, the PDF estimation is done on a grid of Ngrid ⊂ N points, selected non-uniformly through a farthest point sampling method. After the PDF is accurately estimated a density-based clustering algorithm, based on Gaussian Mixture Modeling (GMM), associates each different local maximum in the PDF to a cluster. Once this analysis is completed we obtain the so-called Probabilistic Motifs Identifiers (PMIs) 51 that characterize the features of the system under consideration. The robustness of the clustering analysis (in terms of identified microclusters) is assessed by means of a bootstrapping procedure51. During this step, the SOAP vectors are resampled generating several (N = 73) independent samples of SOAP vectors (sub-datasets).The clustering procedure (KDE and GMM steps) is then performed on each resampled sub-dataset, and the resulting clusters are compared to those obtained using the original (non-resampled and complete) dataset. This comparison results in a stability matrix, which measures how robust the detected clusters are. From this matrix we can build a hierarchical tree (dendrogram) that indicates the adjacency of the obtained microclusters (or the monomer microstates) in the SOAP space. The analysis is performed on snapshots collected along equilibrium MD trajectories, keeping track of the microclusters to which each individual monomer belongs to at each timestep. Thus, this analysis allows us to retrieve information on how structurally similar/different and how dynamically interconnected  the detected microclusters are. The dendrogram of Fig. https://github.com/cosmo-epfl/pamm) modified with a tailored Python3 wrapper for handling the different analysis steps and post-processing.Relevant patterns in the SOAP data are detected by means of unsupervised clustering: we used Probabilistic Analysis of Molecular Motifs (PAMM)dSOAP to merge pairs of clusters in higher-level groups, until the hierarchy of all the systems is completed. This results in the tree-like plot or dendrogram reported in Fig. Hierarchical clustering generally refers to clustering methods that fracture datasets into subsets according to a selected measure of (dis)similarity. In our work we employed the SOAP metric Our analyses build on sampling conformations/transitions during equilibrium MD simulations of the different assembled systems that are studied herein. In general, since in these analyses we reconstruct the internal dynamics and the equilibrium microstates present in these assemblies, a prime requisite for the robustness of the obtained results is that the monomer conformations and transitions are statistically well-sampled, in order to obtain a reliable representation of the equilibrium for each considered assembly (compatibly with the approximations and limits of the molecular models that are used). For this reason, in all systems considered herein, the length of the MD simulations is in the order of microseconds, ensuring that the equilibrium ensemble of each system is well represented by the collected conformational data. In the following we describe the MD approach and sampling protocol that we adopted to ensure this condition for each individual systems simulated herein.72, version 2018.6, from the setup of the simulation box, to the equilibration and production runs. We adopted CG descriptions for each system, built via the Martini scheme58, with explicit solvent description. The parametrization of the monomer models was conducted following the literature data (where available), using the standard, non-polarizable Martini force-field58 (version 2.2). Details per each system are provided in the following. All simulations were performed using Periodic Boundary Conditions (PBC) to limit the finite-size effects, also allowing to simulate portions of infinite aggregates or membranes. To prepare the CG model systems for MD in equilibrium conditions we performed CG-MD equilibration runs of the order of μs in NPT conditions, starting from energy-minimized system conformations. We then proceeded with the production CG-MD runs, to collect the statistics of equilibrium configurations processed in our analyses; all our systems where sampled every 1 ns of CG-MD. For all the CG-MD runs (equilibration and production) we used a 20 fs timestep, an interaction cutoff (1.1 nm) where the non-bonded potentials are truncated and shifted (consistently with the Martini scheme). The Verlet neighbor list scheme73 is employed to reduce the load of calculating pair interactions. The temperature was maintained through the V-rescale thermostat74, set at 300 K for all the simulations, except the cases where a different temperature is stated  with a coupling constant of 1.0 ps. The pressure was maintained via Berendsen barostat75, set at 1 atm with a coupling constant of 2 ps. Isotropic scaling of the simulation cell is adopted when finite aggregates are simulated , while semiisotropic scaling (decoupling x/y from z) is adopted when a portion of an infinite aggregate is considered , as for fibers and membranes.All the simulations presented in this work were performed using the MD package GROMACSIn the following, we report the model references and the specific simulation setup for each system studied.76. We considered three different variants: a water-soluble BTAW  of the monomer core  monomers, already extensively studied in the literature59 monomers ers Fig. A; we stu60 monomers; we studied two variants of these planar, water-soluble monomers, changing the three amino-acids attached to the aromatic core of the molecule. As in ref. 60 we used L-phenylalanine for the first variant . The COG of the monomer cores The supramolecular polymers studied in this work belong to three families, characterized by a specific chemical structure of their monomer functional core. For each family we considered monomer variants:For all the seven fiber-systems we have built an ordered, one-dimensional stack that crosses the PBCs along the principal axis of the aggregate, so that the monomers at the extremes are in contact with each other mimicking and a infinite-fiber portion. Each pre-stacked aggregate was then equilibrated, and a CG-MD production run of about ~2 μs was performed to sample the structural dynamics in equilibrium conditions.56, where SOAP + PAMM characterization of this phospholipid membrane, modeled using different descriptions, is performed. As test cases for the micelle aggregates we selected two surfactant molecules, namely Dodecylphosphocholine (DPC), and Sodium-dodecylsulfate (SDS); both parametrized according to the standard Martini force-field77. The explicit solvent used for these lipid and surfactant systems was Martini water. The equilibrated structure of the surfactant micelles was obtained via spontaneous self-assembly from dispersed monomers, to obtain a size near the normal size distribution at our conditions. The adopted equilibration procedure for the bilayer models is described in ref. 56 (which is the minimization and equilibration protocol given by CHARMM-GUI78). For each system we performed production CG-MD runs of 1 μs of simulation time. For DPPC, DPC and SDS molecules we employed a single-center approach for the SOAP vector calculation, choosing the CG-bead that represents the head of the amphiphilic molecules as SOAP center , “PO4” (DPC) and “SO3” (SDS) beads in Fig. The lipid molecule selected as reference case for two-dimensional aggregates was the dipalmitoylphosphatidylcholine phospholipid  were constructed via 2 μs of CG-MD equilibration run, starting from randomly dispersed monomers in water. During such CG-MD stage the monomers quickly self-assemble and equilibrate forming the spherical structures represented in Fig. The molecule chosen as representative case for spherical aggregates is the alkane hydrocarbon Hexadecane (HEXA); we parametrized this molecule following the Martini force-field standardsSupplementary Information


Environmental surveillance (ES) for poliovirus is increasingly important for polio eradication, often detecting circulating virus before paralytic cases are reported. The sensitivity of ES depends on appropriate selection of sampling sites, which is difficult in low-income countries with informal sewage networks.We measured ES site and sample characteristics in Nigeria during June 2018–May 2019, including sewage physicochemical properties, using a water-quality probe, flow volume, catchment population, and local facilities such as hospitals, schools, and transit hubs. We used mixed-effects logistic regression and machine learning (random forests) to investigate their association with enterovirus isolation (poliovirus and nonpolio enteroviruses) as an indicator of surveillance sensitivity.Four quarterly visits were made to 78 ES sites in 21 states of Nigeria, and ES site characteristic data were matched to 1345 samples with an average enterovirus prevalence among sites of 68% . A larger estimated catchment population, high total dissolved solids, and higher pH were associated with enterovirus detection. A random forests model predicted “good” sites  from measured site characteristics with out-of-sample sensitivity and specificity of 75%.Simple measurement of sewage properties and catchment population estimation could improve ES site selection and increase surveillance sensitivity. Easily measured environmental surveillance site characteristics, including sewage properties recorded with a water-quality probe, predict site sensitivity to detect poliovirus and other enteroviruses. Data collection during site selection could identify better sites and improve the sensitivity of global poliovirus surveillance. Surveillance for poliovirus relies on the detection and reporting of cases of acute flaccid paralysis (AFP), with isolation and sequencing of poliovirus from stool required to confirm diagnosis of poliomyelitis. However, only approximately 1 in 1000 poliovirus infections results in AFP, and the majority of (asymptomatic) infections are thus not detected, allowing “silent” transmission of infection.Poliovirus is shed in stool for 6 weeks on average during asymptomatic infection and may be detected in sewage or wastewater contaminated with fecal material , 2. In pRecognizing the benefits of poliovirus ES as a supplement to AFP surveillance, the Global Polio Eradication Initiative (GPEI) developed a global ES expansion plan for 2013–2018 . At the The sensitivity of ES to detect poliovirus circulation in a given population depends on the nature of the sewage network, the appropriateness of the sampling site, and the quality of sample handling and laboratory processing , 10. HigCurrent GPEI guidelines recommend establishment of ES sites where there is a convergent sewage network and a catchment population of 100 000 to 300 000 people . HoweverTo improve ES site selection and sensitivity, we conducted a study in Nigeria during 2018–2019 to measure ES site characteristics and determine their association with the isolation of human enteroviruses including poliovirus. Our findings inform the next generation of GPEI guidelines for poliovirus ES and are relevant to ES for other pathogens such as typhoid.Five field teams, each consisting of 1 World Health Organization (WHO) and at least 1 national government staff member, made quarterly visits to ES sites across Nigeria, with each team allocated sites in 3 to 5 states after a training workshop in Abuja. Power calculations indicated that to identify an association between a single ES site characteristic and “good” site performance  with 80% power and assuming a large effect size (Cohen’s d = 0.8), we would need to visit 50 sites, assuming half were good and a 5% significance level. If there was an imbalance in the proportion of sites with good performance , this number increased to 59, and for smaller effect sizes further increases in the number of sites were required. Therefore, we planned to visit all 78 ES sites with regular sample collection in Nigeria at the time of study planning (May 2018). At each site, latitude, longitude, and altitude were recorded using a GPS device with ±10-meter accuracy and a photograph of the sampling location was taken. Characteristics of the site on the day of the field team visit were reported using an electronic questionnaire hosted on a mobile phone using Open Data Kit (ODK). Variables recorded were speed of sewage flow, direction of flow, depth and width, color, smell, and open or covered drainage channel. Answers were selected from predefined categories.After completing the questionnaire, the field team recorded water quality parameters from the sewage sampling site using an Aquaprobe AP-2000 with an optional optical turbidity meter included . Parameters recorded included temperature, pH, oxidative reductive potential (ORP), dissolved oxygen, total dissolved solids (TDS), salinity, and turbidity. A protocol for safe and accurate deployment of the water quality probe was developed in advance of the study after pilot testing at the Christian Medical College, Vellore, India. This includes rapid calibration of the probe before visiting the ES site, probe sanitization after use, and instructions on appropriate personal protective equipment. Each field team was allocated a water quality probe, and all probes underwent a full calibration before each quarter of data collection. At least 2 readings were taken at each site visit, and the average of these readings was used in the statistical analysis.Environmental surveillance officers in each state completed an electronic survey at the beginning of the first round of data collection using a mobile ODK application. Survey questions included the date the ES site began operation, usual frequency of sample collection, whether sewage flow varied during the day or seasonally, estimated catchment population and method of estimation, and presence of local public services or infrastructure from a predefined list  and their distance from the site . We also obtained catchment population estimates from the GPEI ES Site Catalogue, which is based on watershed estimates from digital elevation models (DEM) and synthetic and field-collected streams and waterways combined with GRID3 GIS-based population estimates at a 90-meter resolution . In addiWe included laboratory data for ES samples collected between June 1, 2018 and May 31, 2019. Environmental surveillance sample characteristics on arrival in the laboratory are routinely recorded, including the time of sample collection, temperature of the sample carrier, time taken to arrive in the laboratory, sample condition and volume, concentrate volume, and time taken from arrival in the laboratory to inoculation in cell culture. The laboratory algorithm for cell-culture detection of poliovirus and NPEVs in ES samples is described in detail elsewhere , 17.Quarterly data from the field teams were collated together with the ES officer survey data and the laboratory database for individual ES samples. To analyze the association between quarterly data on ES site characteristics and results from individual samples, each sample was matched to site data collected during the quarter corresponding to the date of sample collection .We analyzed quarterly variation in ES site characteristics within and between sites using analysis of variance and assessed linear correlation between variables using Pearson’s correlation coefficient. We used mixed-effects logistic regression to determine the association of site characteristics with enterovirus detection (poliovirus or NPEV). We included a random effect by site to account for repeat observation and a random effect over time  to allow for seasonal trends in circulation of enteroviruses, dividing the country into 3 zones by latitude . We useWe subsequently aggregated enterovirus and ES site characteristic data for the entire study period and used machine learning (random forests) to determine whether site characteristics were able to predict good sites  versus “bad” sites  . We aggram . Environmental surveillance site characteristics collected by the field team including water quality parameters showed some seasonal variation, depending on the measurement  . Sewage depth and width were usually reported as deep (54.9%) and wide (74.7%) and did not show significant variation by quarter . A smell of sewage was reported during 88.3% of ES site visits.Seventy-eight ES sites were visited by the field teams in all 21 states with poliovirus ES at the time of commencing the study . Four visurement . Howeverl <.001) . Sewage The results from the ES officer survey indicated site initiation dates between 2011 and 2018 (mode 2016). The majority of sites were reported to have daily (52 of 78) or seasonal (66 of 78) variation in sewage flow, with increased flow reported in the mornings and during the rainy season. Twenty-two percent (17 of 78) of ES sites reported at least 1 hospital or health facility within a 10-minute walk . Eighty-three percent (65 of 78) reported at least 1 primary or secondary school (mean 3.0), 67% (52 of 78) reported at least 1 transit or commercial hub (mean 2.2), and 21% (62 of 78) reported at least 1 factory (mean 2.4) within a 10-minute walk (means are for those sites reporting at least 1).P = .0542) or the population within 2 km based on WorldPop . Environmental surveillance officer estimates of catchment population size were larger on average than those based on DEM/GRID3 (median size 117 000 vs 26 500) (P = .0145). Catchment population estimates showed limited correlation with water quality parameters , census data (30 of 78), DEM (5 of 78), or an approximation (4 of 78). These catchment population size estimates did not correlate significantly with estimates based on DEM/GRID3  . Digitalrameters .One thousand three hundred forty-five ES samples were collected from sites included in this study between June 1, 2018 and May 31, 2019. The median number of samples collected from a site was 12  and ranged from 9 to 49 . The prevalence of enterovirus isolation, defined as the proportion of samples tested at a site that were positive for any enterovirus (including poliovirus), varied between 9% and 100% (mean 68%) among ES sites . The preIn the mixed-effects logistic regression, the monthly trend in enterovirus detection estimated by the cyclical random walk was strongly seasonal showing a peak in June in the Savanna and Guinea climatic zones and a somewhat later peak in July in the northern Sahel zone . The assThe fit of a single random forests model to the aggregated ES site characteristic data gave an area under the receiver operator characteristic (ROC) curve of 80% indicating reasonable accuracy in correctly classifying ES sites as good (>70% enterovirus isolation) or bad (≤70%) . The curThe prevalence of enterovirus detection including poliovirus and NPEV in ES samples is routinely used as an indicator of ES site sensitivity to detect poliovirus circulation. In Nigeria, 41 of 78 ES sites detected enteroviruses in >70% samples and 67 serotype 2 VDPV were isolated during the study period (compared with 34 serotype 2 VDPV AFP cases in the same states), indicating a sensitive ES system. Nonetheless, 17 (22%) sites detected enteroviruses in less than 50% of samples, suggesting that ES sensitivity could be further improved. In other countries in Africa, the prevalence of enterovirus detection has been considerably lower, further indicating the need for improved guidelines and implementation of ES site selection   , consist= .0179) . In addiEnterovirus prevalence was strongly associated with ES site catchment population estimated using DEM/GRID3 or WorldPop population data but not when estimated by ES officers using vaccination microplans or census data. This suggests that publicly available population data such as WorldPop could be used to help with initial selection of site placement when beginning or expanding poliovirus ES. More detailed planning could then be facilitated by DEM using synthetic or field collected data to demarcate the catchment area—an important consideration when targeting specific high-risk neighborhoods or avoiding overlapping catchments for closely located sites. It is unclear why catchment population estimates from ES officers were larger than DEM/GRID3 estimates, although this may reflect expectations based on WHO guidelines to choose sites with a catchment of 100 000 to 300 000, which is considerably larger than DEM/GRID3 estimates for the majority of sites.Enteroviruses were slightly more prevalent when a smaller sample volume was collected (<1 liter). We speculate that this may reflect an effort by ES officers to collect a larger sample volume when they judge the sewage to be too dilute to allow poliovirus detection.Our study had a number of limitations. Although we were able to quantify key sewage water quality parameters, other measures such as flow speed, depth, and their daily fluctuations were described by subjective categories that may limit comparability between ES sites visited by different teams. Future studies could aim to more accurately quantify these site characteristics using appropriate technology. We also report results from only a single country. To determine whether our findings hold in other settings, it will be important to measure ES site characteristics in other countries, particularly those with lower rates of enterovirus detection. Given the retention of predictive accuracy in the random forests model with data from just a single visit to each ES site, assessment in other countries could be rapid and focus on the key parameters that we have identified in Nigeria . Finally, we used the prevalence of enterovirus isolation on human RD cells as an indicator of human fecal contamination and a proxy of ES site sensitivity. We found that increased catchment population size increased the probability of enterovirus detection. However, single or small numbers of poliovirus infections will shed a limited amount of virus, and this may be diluted to undetectable levels in sewage from large catchment populations . TherefoIf our findings are replicated in other countries, we suggest that the specific and measurable ES site characteristics we have identified should be incorporated into WHO guidelines for the establishment of new ES sites in countries supported by the GPEI. This would facilitate more timely and sensitive poliovirus ES during planned expansion and in response to outbreaks.The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at jiaa175_suppl_Supplementary_Figure_1Click here for additional data file.


The land surface beneath the Greenland and Antarctic Ice Sheets is isostatically suppressed by the mass of the overlying ice. Accurate computation of the land elevation in the absence of ice is important when considering, for example, regional geodynamics, geomorphology, and ice sheet behaviour. Here, we use contemporary compilations of ice thickness and lithospheric effective elastic thickness to calculate the fully re-equilibrated isostatic response of the solid Earth to the complete removal of the Greenland and Antarctic Ice Sheets. We use an elastic plate flexure model to compute the isostatic response to the unloading of the modern ice sheet loads, and a self-gravitating viscoelastic Earth model to make an adjustment for the remaining isostatic disequilibrium driven by ice mass loss since the Last Glacial Maximum. Feedbacks arising from water loading in areas situated below sea level after ice sheet removal are also taken into account. In addition, we quantify the uncertainties in the total isostatic response associated with a range of elastic and viscoelastic Earth properties. We find that the maximum change in bed elevation following full re-equilibration occurs over the centre of the landmasses and is +783 m in Greenland and +936 m in Antarctica. By contrast, areas around the ice margins experience up to 123 m of lowering due to a combination of sea level rise, peripheral bulge collapse, and water loading. The computed isostatic response fields are openly accessible and have a number of applications for studying regional geodynamics, landscape evolution, cryosphere dynamics, and relative sea level change. An important end-member scenario to consider is the total isostatic deformation of the Earth’s solid surface, and resulting change in bed topography, that would arise from the removal of the entire Greenland and Antarctic Ice Sheets.The Greenland and Antarctic Ice Sheets constitute significant loads that isostatically depress the Earth’s land surface by amplitudes of hundreds of metres over wavelengths of thousands of kilometres. Understanding the response of the solid Earth, global gravity field, and rotation axis to changes in ice and ocean loading  is central to the aim of constraining past, present, and future changes in ice sheet behaviour and relative sea level3, (ii) the extent of bed below sea level, seaway connectivity, and marine sediment deposition under ice-free conditions4, (iii) the routing of water and formation of lakes on a pre-glacial landscape5, (iv) interpretations of subglacial geomorphology7, and (v) the relationship between regional geodynamics, tectonic structure, topography, and ice dynamics9.Accurate computation of the fully-rebounded topography of Greenland and Antarctica is important when considering (e.g.): (i) likely nucleation sites of early ice caps during glacial inception12, these products are often not readily accessible and/or utilise ice thickness compilations that have been superseded as data coverage has increased13. They also commonly employ simplified isostatic models with a uniform zero (Airy isostasy) or non-zero flexural rigidity, which fail to capture lateral variations in Earth structure across Greenland and Antarctica. Here, we use contemporary, freely available, compilations of ice thickness and Earth structure to develop isostatic adjustment grids for a fully re-equilibrated, ice-free, Greenland and Antarctica. This new gridded data product can be readily combined with present-day bed elevation measurements to determine the rebounded topography of any region of interest under ice-free conditions.Although a number of studies have computed isostatically rebounded digital elevation models for an ice-free Greenland or Antarctica15  is a proxy for the depth-integrated strength of the lithosphere19, and is a measure of the ability of the lithosphere to support (sub-)surface loads over geological timescales via flexural isostasy20. Te varies significantly on Earth’s continents, with values ranging from 0 to 200 km21, and appears to depend on thermomechanical properties of the lithosphere such as age, thermal history, composition, and state of stress22. Reliable estimates of Te have historically been difficult to establish in the polar regions, given the sparse coverage of geological and geophysical observations and poorly constrained crustal structure. However, we used laterally variable Te grids that have recently been derived using continental-scale spectral analysis of gravity and topography data in Antarctica17 and Greenland16  above an inviscid substrate 24. We therefore used an elastic plate model to determine the vertical displacement resulting from the unloading of the modern ice sheets. The general 2D flexure equation is expressed as20:x and y. E  and ν  are elastic constants, and g is the gravitational acceleration (9.81 m s–2). The load is denoted by h, and the flexural deformation is given by w. We assumed standard ice load (ρload) and mantle (ρmantle) densities of 917 and 3330 kg m–3, respectively. The ρdisplace and ρinfill terms refer to the density of the material displaced by the load and infilling the flexural moat, respectively. In this study, the displaced/infilling material is either air (0 kg m–3) or seawater (1028 kg m–3), depending on the situation being considered.To model the response of the solid Earth to the removal of the modern ice sheets, we assumed that the lithosphere and mantle have attained long-term isostatic equilibrium following full deglaciation. We assumed that transient viscoelastic mantle responses have completely relaxed, which would typically be achieved over timescales on the order of 100 kyrTe scenarios of Steffen et al. (2018)16 and Swain & Kirby (2021)17 using a centred finite difference technique25. Steffen et al. (2018) place uncertainties of up to 40 km on the spectral Te estimates for Greenland, but uncertainties are not quantified for the Antarctic estimates. To determine a simplified measure of the uncertainty in the flexure, we also solved Eq.  to the present-day. We used a eustatic sea level curve to capture the growth of ice up to the LGM (26 ka)27, and the ICE-6G_C model for subsequent deglaciation28. We calculated the residual disequilibrium in the solid Earth deformation and the geoid (sea surface) height change (which is caused by shifts in the distribution of mass within the Earth).In addition to the deformation induced by modern ice load removal, we applied a correction for the ongoing unequilibrated response of the solid Earth to the loss of ice since the Last Glacial Maximum (LGM). Because we sought to determine the residual part of the post-LGM isostatic response that has not yet equilibrated, we calculated the time-dependent viscoelastic response that is still to come due to the LGM-to-present ice load change using a self-gravitating viscous Earth model, following the approach described in ref22 Pa s), upper mantle viscosity (3.0 and 5.0 × 1020 Pa s), and elastic lithosphere thickness (71 and 96 km). These ranges of values reflect typical spatially-averaged estimates for the entirety of Greenland and Antarctica31. Note that this lithosphere thickness is not equivalent to Te; it instead describes the thickness of an idealised elastic layer above the viscoelastic mantle appropriate for ice age timescales32. For the above range of mantle viscosities, the timescale required for the residual disequilibrium to decay by a factor of 1/5e from its post-LGM peak value and thereby reach approximate steady state varies from 30 to 70 kyr.To allow for uncertainties in the viscoelastic structure of the mantle beneath Antarctica and Greenland, the unequilibrated solid surface deformation and geoid change were assumed to be the mean responses of a suite of viscoelastic Earth models, with 24 combinations of lower mantle viscosity  and an additional non-uniform sea surface height change caused by the residual post-LGM re-equilibration of the geoid. We neglected the rotational and gravitational feedbacks associated with loss of mass from the modern ice sheets, because in this case we are moving from one equilibrium state to another (modern ice to no ice) and the gravity change due to ice mass loss is largely cancelled out by the gravity change due to solid Earth deformation. The flexural response to water loading was computed using Eq. :Te) used to compute the long-term flexural response to the removal of the modern ice load and the water loading feedbacks.The contributions of modern ice sheet unloading, post-LGM disequilibrium, and water loading feedback Fig.  were sumTe scenarios  , the pattern of flexure is largely independent of Te  lateral heterogeneity in Te, (ii) a correction for post-LGM disequilibrium, and (iii) iterative computation of the isostatic effect of water loading. For example, the low Te values that prevail in West Antarctica result in shorter-wavelength variability in isostatic uplift than in models that assume a uniformly high Te for all of Antarctica10. Our isostatic response calculations also use the most recent high resolution modern ice thickness compilations15, and can be readily updated alongside future releases of improved ice thickness (and Earth structure) data products.The isostatic response to ice unloading in Greenland and/or Antarctica has been computed in a number of previous studies, typically as an intermediate step as part of a wider geoscientific analysis35. Fully-rebounded bed elevation  (un)loading, and not the landscape-modifying effects of erosion, sedimentation, tectonic deformation, and lithosphere heating/cooling39. Instead, these updated adjustments for ice unloading represent the first stage in the reconstruction of a pre-glacial landscape, and as such may be integrated into future generations of palaeo-topographic and -bathymetric reconstructions.Finally, quantification of the ice-free elevation of Greenland and Antarctica is key for understanding the contributions of crustal compensation and mantle dynamics to the support of the present-day topographyIn this study we used recent compilations of ice thickness and lithospheric effective elastic thickness to compute the total equilibrated isostatic response to the complete unloading of the Greenland and Antarctic Ice Sheets. We envisage that these newly constructed grid files of the total isostatic response and the associated uncertainty can be used to correct bed elevations for the isostatic effects of ice loading at individual point locations, along geophysical survey tracks, and across wider regions up to and including the entirety of the landmasses. The gridded data products, which are openly accessible, will thus be of value to studies focussing on  ice sheet dynamics, landscape evolution, hydrological flow routing and drainage patterns, palaeo-environments, subglacial geology, tectonics, and geodynamics in the polar regions.


Different preparations of IgG anti-IgE purified from patients with AD and rabbit IgG anti-IgE were compared for their triggering effects on the in vitro release of histamine and type 2 cytokines  from basophils and of histamine and lipid mediators (prostaglandin D2 and cysteinyl leukotriene C4) from human skin and lung mast cells. One preparation of human IgG anti-IgE out of six patients with AD induced histamine release from basophils, skin and lung mast cells. This preparation of human IgG anti-IgE induced the secretion of cytokines and eicosanoids from basophils and mast cells, respectively. Human monoclonal IgE was a competitive antagonist of both human and rabbit IgG anti-IgE. Human anti-IgE was more potent than rabbit anti-IgE for IL-4 and IL-13 production by basophils and histamine, prostaglandin D2 and leukotriene C4 release from mast cells. Functional anti-IgE autoantibodies rarely occur in patients with AD. When present, they induce the release of proinflammatory mediators and cytokines from basophils and mast cells, thereby possibly contributing to sustained IgE-dependent inflammation in at least a subset of patients with this disorder.IgE-mediated release of proinflammatory mediators and cytokines from basophils and mast cells is a central event in allergic disorders. Several groups of investigators have demonstrated the presence of autoantibodies against IgE and/or FcεRI in patients with chronic spontaneous urticaria. By contrast, the prevalence and functional activity of anti-IgE autoantibodies in atopic dermatitis (AD) are largely unknown. We evaluated the ability of IgG anti-IgE from patients with AD to induce the  The inteceptors , 35. Aggeceptors , 37, or eceptors –41 leadsSeveral studies have reported the presence of spontaneously occurring autoantibodies to IgE , 42–45, in vitro has become a mainstay of research in allergy and immunology for some compelling reasons. First, these cells can play critical roles in the activation of type 2 immune responses through the production of such Th2-like cytokines as IL-4 and IL-13  from human basophils. Third, we investigated whether human monoclonal IgE is a competitive antagonist of human and rabbit IgG anti-IgE. Finally, we examined the ability of functional human IgG anti-IgE to activate human primary skin and lung mast cells.4 , hyaluronidase, chymopapain, elastase type I, cysteinyl leukotriene C4 (LTC4), and prostaglandin D2 (PGD2) , deoxyribonuclease I , (3H)-LTC4 and (3H)-PGD2  were commercially purchased. Rabbit IgG anti-IgE antibody, produced by rabbit immunization with the Fc fragment of a human IgE myeloma (patient PS) and then absorbed with the IgE Fab as previously described . The Pipes buffer used in these experiments was a mixture of 25 mM Pipes, 110 mM NaCl, 5 mM KCl, pH 7.37, referred to as P. P2CG contains, in addition to P, 2 mM CaCl2 and 1 g/L dextrose  (Pipes), L-glutamine, antibiotic-antimycotic solution , collagenase , Hanks’ balanced salt solution, fetal calf serum (FCS) , pronase , RPMI 1640 with 25 mM HEPES buffer, Eagle’s minimum essential medium , Percoll , CD117 MicroBeads , Iscove modified Dulbecco Medium (IMDM) , HClOescribed , was kindextrose ; pH was The study was approved by the Ethics Committee of the University of Naples Federico II, School of Medicine (Prot. 198/18), and informed consent was obtained from all participants prior to collection of blood according to recommendations from the Declaration of Helsinki. Serum samples from six patients with AD (aged 5 to 17 years) and six normal donors (aged 6 to 22 years) were collected and stored at -20°C. Patients with AD had similar clinical pictures, characterized by a chronic, pruritic skin eruption marked by erythema, papules, or lichenification of flexural areas of the extremities, face and neck . Serum sIgE myeloma protein was purified from a myeloma patient  by gel f4SO4 followed by chromatography on a DEAE-cellulose column equilibrated with 0.01 M phosphate buffer (pH 7.9), as previously described , much higher than in nonatopic controls (< 50 ng/ml) . For affBasophils were purified from peripheral blood of healthy volunteers, aged 19-45 years, undergoing hemapheresis within the Immunohematology Unit at the University of Naples Federico II. Buffy coats were subjected to double-Percoll density centrifugation, which produced basophil-depleted and basophil-enriched cell suspensions . Basophi6 mast cells/g of wet tissue, and purity was between 5 and 10%. Human skin mast cells (HSMCs) were further purified using a CD117 MicroBead kit cell sorting system  according to the manufacturer’s instructions. Mast cell purity using this technique ranged from 36 to 71% as assessed by Alcian blue staining.The study was approved by the Ethics Committee of the University of Naples Federico II (Protocol: Human MC No. 7/19) and informed consent was obtained from all donors. Skin obtained from patients undergoing either mastectomy for breast cancer or elective cosmetic surgery was separated from the subcutaneous fat by blunt dissection. The tissue was finely cut into 1- to 2-mm fragments and dispersed into single-cell suspension as previously described . Yields 6 cells/mL) in RPMI 1640 with 5% FCS, 2 mM L-glutamine, and 1% antibiotic-antimycotic solution and incubated in 24-well plates . The enzymatic tissue dispersion yielded ≈5 × 105 mast cells/gram of lung tissue and purity ranged from 4% to 19% (Human lung mast cells (HLMCs) were purified from macroscopically normal lung tissue obtained from patients  affected by lung adenocarcinoma undergoing thoracic surgery , 75. Fre4, minus the basal, or spontaneous release (MAX), and threshold sensitivity (HRSENS), that is, 100x the inverse of the secretagogue concentration inducing half-maximal HR (EC50), were calculated as described  were resuspended in P2CG. 0.3 mL of the cell suspensions were placed in 12 × 75 mm polyethylene tubes. 0.2 mL of each prewarmed releasing stimulus was added, and incubation was continued at 37°C for 45 min  . The ELISA detection range was 31-2,000 pg/ml (IL-4) and 125-4,000 pg/ml (IL-13).4 and PGD2 were measured in duplicate samples by radioimmunoassay .Data were analyzed with the GraphPad Prism 8 software package . Values were expressed as mean ± SEM (standard error of the mean). Statistical analysis was performed using Student’s t-test or one-way analysis of variance. Values were considered significant when the probability was below the 5% confidence level (-4 to 3 x 10-2 μg/ml) of human IgG anti-IgE isolated from only one out of six AD patients, as previously described (-3 to 3 x 10-1 μg/ml) in parallel experiments with the same basophil preparations (-3 to 3 μg/ml) purified from six healthy donors failed to induce mediator release from basophils (MAX) in response to human IgG anti-IgE (70.0% ± 3.80%), was similar to basophil reactivity to rabbit IgG anti-IgE (65.8% ± 3.68%). By contrast, the secretagogue concentration inducing half-maximal histamine release (EC50) induced by the functionally active human anti-IgE preparation (2.4 x 10-3 ± 5 x 10-4 μg/ml) was significantly lower than the corresponding concentration of rabbit anti-IgE (4 x 10-2 ± 1 x 10-2 μg/ml), hence resulting in significantly higher HRSENS (p < 0.05). These results indicate that one preparation of the human IgG anti-IgE preparations tested was active on human basophils. This preparation of human IgG anti-IgE is from now on referred to as “human anti-IgE”.In a first group of experiments, we compared the effects of increasing concentrations of human IgG anti-IgE purified from the sera of six patients with AD, rabbit IgG anti-IgE and human polyclonal IgG on HR from human basophils. escribed , induced-3 to 10-1 μg/ml) of human and rabbit anti-IgE. In these experiments, basophils were incubated 4 hours at 37°C to evaluate IL-4 release, whereas they were incubated 18 hours at 37°C to examine IL-13 production, as previously reported  mig mig70) mde novo synthesis of PGD2 by HSMCs (2 induced by human anti-IgE (31.1 ± 3.7 ng/106 cells) was comparable to that caused by rabbit anti-IgE (30.5 ± 2.6 ng/106 cells). By contrast, the secretagogue concentration inducing half-maximal histamine release (EC50) for histamine release was significantly lower (5 x 10-2 ± 1 x 10-2 μg/ml) for human anti-IgE compared to rabbit anti-IgE (2.5 x 10-1 ± 6 x 10-2 μg/ml) (p < 0.05), indicating a comparably higher HRSENS. Similarly, the EC50 for PGD2 production caused by human anti-IgE (7.2 x 10-2 ± 2.1 x 10-3 μg/ml) was significantly lower than that of rabbit anti-IgE (2.9 x 10-1 ± 3 x 10-2 μg/ml) (p < 0.05).In five parallel experiments, we compared the activating properties of human and rabbit anti-IgE on HR (de novo synthesis of LTC4 and PGD2 from HLMCs. Increasing concentrations (10-2 to 3x10-1 μg/ml) of human or rabbit anti-IgE (10-1 to 3 μg/ml) caused a concentration-dependent release of histamine from HLMCs (50 was significantly lower (4.6 x 10-2 ± 4 x 10-3 μg/ml) for human compared to rabbit anti-IgE (3.4 x 10-1 ± 8 x 10-2 μg/ml) (p < 0.01). In these experiments, we also compared the effects of human and rabbit anti-IgE on the de novo synthesis of LTC4 and PGD2 from HLMCs. 4 by HLMCs exposed to human anti-IgE (40.9 ± 2.2 ng/106 cells) was similar to that caused by rabbit anti-IgE (42.5 ± 2.0 ng/106 cells). By contrast, the concentration of human anti-IgE inducing half-maximal LTC4 release was significantly lower (4.0 x 10-2 ± 4 x 10-3 μg/ml) than the EC50 for rabbit anti-IgE (2.5 x 10-1 ± 6 x 10-2 μg/ml) (p < 0.05). Similarly, HLMC reactivity to human anti-IgE (31.4 ± 2.6 ng/106 cells) was similar to rabbit anti-IgE (38.9 ± 3.0 ng/106 cells) with respect to PGD2 production (50 for PGD2 production caused by human anti-IgE (4.2 x 10-2 ± 1 x 10-3 μg/ml) was significantly lower than that of rabbit anti-IgE (2.8 x 10-1 ± 8 x 10-2 μg/ml) (p < 0.05).In five experiments, we compared the effects of increasing concentrations of human and rabbit anti-IgE on HR and Our results indicate that although autoantibodies against IgE can be found in some patients with AD, these can rarely induce the activation of human basophils and mast cells. We have detected functional IgG anti-IgE in one out of six patients with AD and characterized its ability to trigger mediator release from human basophils and mast cells. This human IgG anti-IgE is a more potent secretagogue than rabbit IgG anti-IgE, and human monoclonal IgE appears to act as a competitive antagonist of either antibody. A novel finding emerging from this study is the ability of human anti-IgE from AD to induce the release of IL-4 and IL-13 from human basophils. Another novel aspect is the observation that human anti-IgE activates not only human basophils, but also skin and lung mast cells to release histamine and arachidonic acid metabolites.in vitro . Collectively, these results indicate that human anti-IgE, when it is functionally present, can be significantly more potent than rabbit anti-IgE preparations commonly used in experimental or diagnostic in vitro protocols for IgE-dependent activation of human FcεRI+ cells.Our results provide some information on the functional potency of the IgG anti-IgE isolated from a patient with AD. Although basophil reactivity, that is the maximal HR in response to human anti-IgE, was similar to that induced by rabbit anti-IgE, the potency of human anti-IgE was significantly higher than that of rabbit anti-IgE. Similar results were obtained when comparing the reactivity and threshold sensitivity of human skin and lung mast cells to human and rabbit anti-IgE in experiments looking not only at the HR but also the We also provide some clues on the immunologic mechanism of activation of human basophils by human IgG anti-IgE. We found that preincubation of human basophils with increasing concentrations of human monoclonal IgE purified from a myeloma patient , 70 concA novel finding of this study is the ability of human IgG anti-IgE to induce the release of Th2-like cytokines  from human basophils. The vast majority of studies exploring the functional activity of human anti-IgE and anti-FcεRI have evaluated the ability of these autoantibodies to induce HR from human basophils , 90, 91.4 plays a role in a mouse model of AD  and lipid mediators  from human basophils and/or mast cells. Further studies in larger cohorts of patients with different phenotypes of AD are needed to more conclusively assess the prevalence of functional autoantibodies to IgE or FcεRI and their possible contribution to disease pathogenesis and the response to current and prospective therapeutic strategies.In conclusion, our results extend previous findings , 44 indiThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Naples Federico II, School of Medicine (Prot. 198/18), and informed consent was obtained from all participants prior to collection of blood according to recommendations from the Declaration of Helsinki. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.RP, IQ, GM, VC, and GV designed the research. RP, IQ, AP, and VC did the experiment. RP, GM, MT, VC, and GV analyzed the data and wrote the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This work was supported in part by grants from the CISI-Lab Project (University of Naples Federico II) and TIMING Project and Campania Bioscience (Regione Campania).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Nicotiana spp.) and in Banisteriopsis caapi, a vine used in the Amazonian ayahuasca brew. The β-carbolines present in B. caapi may have effects on neurogenesis and intrinsic antidepressant properties, in addition to potentiating the bioavailability of the hallucinogen N,N-dimethyltryptamine (DMT), which is often present in admixture plants of ayahuasca such as Psychotria viridis. Tobacco also contains physiologically relevant concentrations of β-carbolines, which potentially contribute to its psychopharmacology. However, in both cases, the threshold of MAO inhibition sufficient to interact with biogenic amine neurotransmission remains to be established. An important class of antidepressant medications provoke a complete and irreversible inhibition of MAO-A/B, and such complete inhibition is almost unattainable with reversible and competitive inhibitors such as β-carbolines. However, the preclinical and clinical observations with synthetic MAO inhibitors present a background for obtaining a better understanding of the polypharmacologies of tobacco and ayahuasca. Furthermore, MAO inhibitors of diverse structures are present in a wide variety of medicinal plants, but their pharmacological relevance in many instances remains to be established.The monoamine oxidases (MAOs) are flavin-containing amine oxidoreductases responsible for metabolism of many biogenic amine molecules in the brain and peripheral tissues. Whereas serotonin is the preferred substrate of MAO-A, phenylethylamine is metabolized by MAO-B, and dopamine and tyramine are nearly ambivalent with respect to the two isozymes. β-Carboline alkaloids such as harmine, harman(e), and norharman(e) are MAO inhibitors present in many plant materials, including foodstuffs, medicinal plants, and intoxicants, notably in tobacco ( N,N-dimethyltryptamine (DMT); although from a traditional Amazonian perspective this may not be the only essential mechanism of action, and ayahuasca brew samples need not invariably contain DMT (The monoamine oxidases (MAOs EC 1.4.3.4) are flavin-containing amine oxidoreductases that occur in the outer mitochondrial membrane of most mammalian cells. MAOs deaminate their endogenous substrates in the presence of molecular oxygen; this generates the corresponding aldehyde-intermediates, which further metabolize to carboxylic acids or alcohol derivatives. The deamination step releases the amine nitrogen as ammonia and with the production of hydrogen peroxide. As such, MAO is a double-edged sword, ridding the organism of excess biogenic monoamine compounds, while also producing potentially toxic metabolites. MAO occurs in MAO-A and MAO-B isozymes with slightly divergent amino acid sequences, and having similar genetic organization with 15 exons on the X-chromosome . Apparentain DMT , we prestain DMT .MAO inhibitors fall into various categories with respect to the nature of the binding to the enzyme. Certain molecules are MAO substrates that can exert competitive inhibition of the enzyme by virtue of their occupation of the binding pocket, and other compounds, like the β-carbolines, are reversibly binding competitive inhibitors that are not substrates for MAO. Still other compounds, like pargyline, clorgyline, and rasagiline, are irreversible MAO inhibitors, also known as suicide substrates. This class of MAO inhibitor are substrates that mechanistically form a covalent bond with the enzyme via the propargyl moiety; this reaction permanently kills the enzyme. To this day, there is a prevailing notion that pharmacologically blocking the breakdown of dopamine, serotonin, and other MAO substrates should underlie the antidepressant property of MAO inhibitors by rectifying some neurochemical deficiency, despite the paucity of evidence for any such deficiency syndrome in patients with depression . Be thatL-deprenyl had no such effect  inhibits MAO-A/B at µM concentrations (Glycyrrhiza glabra) extracts are significantly potent inhibitors of MAO-B in vitro (Hypericum perforatum) has long served as an antidepressant in traditional medicine, an effect often attributed to MAO-inhibition by one or more of its constituents. Floral extracts indeed inhibited MAO-A in vitro (IC50 65 μg/ml), which proved to be due to quercetin and flavonoids rather than hypericin , which is a hallucinogenic serotonin 5-HT2A agonist otherwise subject to rapid deamination in the gut following oral administration (Psychotria viridis (Diploterys cabrerana (Mimosa tenuiflora (B. caapi and P. viridis) , but only traces of β-carbolines  , evokes armaline . In the rbolines , an obsedosis sola facit venenum (the dose makes the poison). In our considerations of MAO interactions, we must be mindful that MAO inhibitors, despite their ubiquity in plants, may not be sufficiently active to have pronounced effects on biogenic amine metabolism in the living organism. For example, sensitive analyses reveal the presence of harmane and norharmane at low concentrations (<50 ng/g) in a wide variety of foodstuffs, and moderately high levels in coffee beverages (200 ng/ml). The first attestation of the presence of harmane and norharmane in tobacco, and its apparent pyrolytic formation from tryptophan in the tobacco, dates to 1962  and MAO-B (IC50 45 µM), as does eugenol (2-methoxy-4-(prop-2-en-1-yl)phenol), the main odorant of clove oil  or carbon-11 . The radioactive decay with emission of a positron in the medium of the brain is followed swiftly by annihilation of the positron upon meeting its counterpart electron; the entire energy bound up in the mass of the electron-positron pair (E = mc2) then converts into two photons of enormously high energy, approximately 512 keV. These photons, commonly known as gamma rays, fly apart at an angle of just about 180°. If such an emission occurs when an individual’s head is within the aperture of the PET instrument, this counts as “an event”. The accumulating recording of millions of such events over time gives rise to a dynamic PET image, analysis of which maps out the uptake and distribution of binding sites for the tracer molecule. Arguably, MAO-PET was one of the earliest successful uses of this molecular imaging technology to yield new information about human physiology (Molecular imaging by positron emission tomography (PET) affords a window on the living brain, whereby the uptake and binding of selective ligands reveals the molecular targets. We can properly understand PET as a binding assay ysiology .11C]MPTP  in the molecule -one, which has nM affinity in vitro , showing a mean 60% reduction of MAO-A activity in cerebral cortex of tobacco smokers, versus a 40% reduction in subcortical regions. Deuterium-substituted [11C]deprenyl PET revealed a similar reduction of MAO-B availability in brain of smokers , then declining with a half-life of about 1 h . This lodeprenyl ; the hardeprenyl ; it rema= spleen . This ubin vitro indicated the β-carboline [11C]harmine to have good properties for detection of MAO-A sites in tissue specimens  decreased levels of serotonin transporters in brain of depressed individuals compared to age-matched controls (Returning to the theme of [ructures . Examinacontrols .11C]harmine at tracer doses (in vitro (circa 1 nM), we can predict substantial MAO-A inhibition throughout the body and lasting several hours after a dose of 0.5 mg/kg harmine.We have focused in the preceding section on the molecular imaging of MAO, and its possible relation to neurotransmission and behavior. The physiological relevance of partial MAO-inhibition is a key consideration. Therapy with MAO inhibitors usually aims to obtain nearly complete inhibition of MAO in brain . How important, then, is the transient and partial MAO inhibition seen in the context of tobacco smoking? More dramatically, harmine or other β-carboline alkaloids potentiate the hallucinogenic action of DMT in the context of ayahuasca, presumably by a peripheral action of blocking DMT metabolism in the gut. However, central MAO inhibition might be an independent aspect of the psychopharmacology of harmine in ayahuasca mixtures. Unfortunately, there is scant information about the pharmacokinetics of harmine. An early study followed the disposition of harmine and its metabolites following intravenous injection in humans (0.5 mg/kg) and rats (5 mg/kg) . Much aser doses , there wer doses . The harB. caapi is an essential component of ayahuasca, the importance of DMT and other alkaloids in P. virdis is less clear. In an ayahuasca study, a group of 15 male volunteers (mean body weight 75 kg) consumed 150 ml of a decoction containing 30 mg harmaline, 250 mg harmine and 159 mg tetrahydroharmine, as well as 35 mg DMT  resulted in plasma harmaline levels above 10 ng/ml persisting for at least 12 h, whereas harmine levels were around 100 ng/ml for a similar period, but harmol levels were generally below 10 ng/ml. The corresponding concentrations were much lower in rats treated with a low P. harmala dose (15 mg/kg), with harmaline and harmine being barely detectable in plasma. This suggests a non-linear dose-response, making it difficult to extrapolate these results to human ingestion of P. harmala; there is a case report of fatal toxicity due to P. harmala ingestion  the notorious toad toxin 5-methoxy-N,N-dimethyltryptamine  and harmine   . HoweverL; 1 µM) . Severe B. caapi without admixture plants, in the context of healing and initiatory rites (B. caapi were found for instance to stimulate neurogenesis in adult mice (Diospyros melanoxylon) contain pharmacologically active compounds? Clove cigarettes (kreteks), which are popular in Indonesia, contain half as much nicotine as typical cigarettes, but their aromatic quality may encourage deeper inhalation and thus greater absorption of alkaloids  in the Amazon region is considered an important medicinal plant and is a frequent component of Amerindian ethnobotanical mixtures, also elsewhere. In traditional Amazonian medicine, this tobacco species is prepared as a liquid remedy and is ingested orally to treat mental health and other ailments (N. rustica.In summary, β-carbolines and other classes of MAO inhibitors are present in tobacco and many other plants, and likely contribute to the psychopharmacology of tobacco usage and perhaps dependence. However, there is incomplete documentation of the dose-response relationships; a low degree of MAO inhibition may potentiate the reinforcing properties of nicotine or amphetamine, but complete MAO blockade may be aversive. There is a particular involvement of MAO-A inhibition in the pharmacology of ayahuasca, which is generally attributed to its potentiation of the bioavailability of the actively hallucinogenic ingredient DMT. However, not all ayahuasca preparations contain significant amounts of DMT , and somry rites ; we needult mice  and harmult mice . Furtherult mice . Is thislkaloids . Where tailments ; the rapIn this account, we have emphasized the family resemblance between tobacco and ayahuasca; both substances contain a mixture of alkaloids, with considerable potential for interaction, especially through the inhibition of MAO by β-carbolines and other competitors. We may inadvertently have raised more questions than we have answered, but we see a clear need for a more systematic examination of the separate and combined contributions of polypharmacy in the contexts or tobacco or ayahuasca consumption.


Nucleic acid reagents, including plasmid-encoded genes and small interfering RNA (siRNA), are promising tools for validating gene function and for the development of therapeutic agents. Native β-cyclodextrins (BCDs) have limited efficiency in gene delivery due to their instable complexes with nucleic acid. We hypothesized that cationic BCD nanoparticles could be an efficient carrier for both DNA and siRNA. Tetraethylenepentamine-coated β-cyclodextrin (TEPA-BCD) nanoparticles were synthesized, characterized, and evaluated for targeted cell delivery of plasmid DNA and siRNA. The cationic TEPA coating provided ideal zeta potential and effective nucleic acid binding ability. When transfecting plasmid encoding green fluorescent protein (GFP) by TEPA-BCD, excellent GFP expression could be achieved in multiple cell lines. In addition, siRNA transfected by TEPA-BCD suppressed target GFP gene expression. We showed that TEPA-BCD internalization was mediated by energy-dependent endocytosis via both clathrin-dependent and caveolin-dependent endocytic pathways. TEPA-BCD nanoparticles provide an effective means of nucleic acid delivery and can act as potential carriers in future pharmaceutical application. Gene expression could be modulated through the use of exogenous nucleic acids. The power of nucleic-acid-based drugs, including DNA molecules and small interfering RNAs (siRNAs), lies in their abilities to specifically enhance or silence genes of interest. The siRNA-based drugs were recently approved by US Federal Drug Administration , markingNonviral gene delivery vectors have been shown to have several advantages, such as ease of synthesis, mass production, versatile modification, and low immunogenicity . There aCationic polysaccharides can bind with siRNAs to form complexes, thereby protecting siRNA from degradation and neutralizing their negative charges . NanoparIn this study, we developed an easy and efficient strategy to generate TEPA-BCD. The 6-hydroxyl groups of BCD were activated with tosyl chloride. After nucleophilic displacement by ethylenediamine (EDA), TEPA-BCD nanoparticles were synthesized by the crosslinking reaction of EDA-BCD and TEPA via glutaraldehyde (GA). The synthesis of TEPA-BCD nanoparticles is demonstrated in −1 , 1036 cm−1 (C–O vibration). Another two intense peaks around 1036 and 2900 cm−1 from C-O and C–H stretching were also found in all BCD derivatives indicating the main BCD structure was maintained after the amine introduction .,45.44,45CPZ blocked clathrin-coated pit formation , and conDevelopment of siRNA delivery systems depends not only on their transfection efficiencies, but also on their gene-silencing effect. Although both Gen and MCD treatments showed no reduction in plasmid DNA transfection efficiency C, the geIn the intravenous delivery, the nanoparticles have to escape the various barriers such as kidney filtration, phagocytosis, and hydrolysis degradation. Sequentially, they should transport across vascular vessels, diffuse through the extracellular matrix, and reach the target cells . The enhIn summary, we developed a cationic BCD-based nanoparticle delivery system and applied it in the fibroblast 3T3 cell line and the epithelial ARPE cell line. Our results demonstrated the multifunctionality critical for internalization and activity of both gene-coded plasmid DNA and siRNA. When the TEPA-BCD/plasmid ratio was 1.71:1, the nanoparticles had a transfection efficiency of 97%, and cells maintained viability around 83%. When the TEPA-BCD/siRNA ratio was 96:1, it knocked down the fluorescent expression by 57%, yet the cell viability remained around 82%. TEPA-modified BCD demonstrated a nanometer size, positive surface charge, minimal cytotoxicity, great electrostatic interaction with nucleic acid, and efficient delivery. In both ARPE cells and 3T3 cells, TEPA-BCD entered cells through clathrin- and caveolae-mediated endocytosis. TEPA-BCD/plasmid DNA mediated high gene expression and TEPA-BCD/siRNA displayed efficient gene silencing. The TEPA-BCD provides a promising platform for delivery of therapeutic nucleic acids in vitro and warrants further development of its potential for in vivo delivery.BCD was a generous gift from Feng-Yuan Biotech . Acetone, methanol, and ethanol were purchased from Echo Chemicals . Green fluorescent protein (GFP)-expressing plasmid was obtained from Takara Bio . Fetal bovine serum (FBS) was purchased from Biological Industries . The siRNA against GFP gene  was purchased from MDBio . DNA and siRNA transfection controls (PolyJet and GenMute) are purchased from SignaGen . TEPA, p-tolylsulfonyl chloride, sodium chloride, and other chemicals were purchased from Sigma-Aldrich . All reagents were used without further purification.1H NMR  of BCD: δ = 5.69 , 4.84 , 4.44 , 3.62 , 3.33  ppm. 13C NMR  of BCD: δ = 102.04 (C-1), 82.01 (C-4), 73.51–72.51 , 69.63 , 60.37 (C-6) ppm. 1H NMR  of Tosyl-BCD: δ = 7.12 , 7.45 , 5.70 , 4.80 , 4.50 , 4.32 , 4.21 , 3.48 , 2.43  ppm. 13C NMR  of Tosyl-BCD: δ = 128.48 , 102.44 (C-1), 82.02 (C-4), 73.55–72.50 , 60.41 (C-6), 31.16 (Tosyl-CH3) ppm. 1H NMR  of EDA-BCD: δ = 5.70 , 4.84 , 4.44 , 3.48 , 2.1  ppm. 13C NMR  of EDA-BCD: δ = 101.82 (C-1), 81.09 (C-4), 73.04–71.78 , 60.25 (C-6), 42.04 (BCD-C-C-N) ppm. 1H NMR  of TEPA-BCD: 5.06 , 4.77 , 4.47 , 3.74 , 3.19 , 2.84 , 2.22  ppm. 13C NMR  of TEPA-BCD: δ = 102.10 (C-1), 81.24 (C-4), 73.20–72.08 , 60.15 (C-6), 45.22 (C-NH), 30.24 (cyclic ether C-C-O) ppm.Mono-6-deoxy-6-p-tolylsulfonyl (tosyl)-BCD and EDA-BCD were synthesized as described previously ,50. BrieE. coli cells. The host E. coli was grown in LB broth with kanamycin (50 µg/mL) and the plasmid DNA was isolated using Mini Purification Kit . The concentration and quality of nucleic acids were determined using a NanoDrop spectrophotometer  and the Beer–Lambert law with an extinction coefficient of 50 μg/mL−1 cm−1 at 260 nm. Isolated plasmid DNA had OD 260/280 ratios of 1.80–1.95, indicating the quality was suitable for our application. Polyplexes were formulated by mixing 0.21–1.71 μg (10–80 μL) TEPA-BCD with plasmid DNA (1 μg) or siRNA  in 100 μL DMEM medium.We transformed plasmid GFP-C3 into DH-5α To visualize morphology of BCD derivatives, diluted samples were adsorbed onto a 100 mesh copper grid and dried overnight, followed by imaging on a transmission electron microscope . The magnification power of TEM is 50,000. The chemical groups of modified BCDs were investigated using Fourier transform infrared spectroscopy  and an NMR spectrometer (Bruker AV III HD). The size and zeta potentials of the TEPA-BCD complexes were measured using ZetaSizer with 633 nm He-Ne Laser  and the folded capillary cell .ARPE cells  and 3T3 cells (a mouse embryonic fibroblast cell line) from the cell bank of BCRC  were used for GFP plasmid delivery experiments. DNA transfection was performed as previously reported . APRE-GF4 cells/well. When reaching 90% confluence, the cells were treated with polyplex and endocytic inhibitor for 3 h. Polyplexes were formulated by mixing 1.28 μg TEPA-BCD with plasmid DNA (1 μg) or siRNA (13.3 ng) in 100 μL DMEM medium. Subsequently, the transfection medium was replaced with fresh DMEM supplemented with 10% FBS and cells were incubated for another 24 h. Imaging was conducted on an automated image analyzer for analysis of fluorescence intensity and cell viability.The cellular uptake mechanisms of polyplex were examined using different endocytic inhibitors: chlorpromazine (30 µM), genistein (200 µM), sodium azide (10 mg/mL), methyl-BCD (10 mM), and monensin (3 µM). The cells were seeded in 48-well plates at 5 × 10t-test by Analysis Toolpak in Microsoft Excel. Statistical significance is represented as * p < 0.05.All cellular data were performed by two individual experiments. The Zetasizer results were the average from three individual experiments. The mean and standard deviation were calculated using Microsoft Excel. Statistical comparisons are performed using PROC ANOVA followed by the Tukey post hoc test using SAS 9.4 software  and unpaired two-tailed Student