Divalent Multilinking Bonds Control Growth and Morphology of Nanopolymers.

Assembly of nanoscale objects into linear architectures resembling molecular polymers is a basic organization resulting from divalent interactions. Such linear architectures occur for particles with two binding patches on opposite sides, known as Janus particles. However, unlike molecular systems where valence bonds can be envisioned as pointlike interactions nanoscale patches are often realized through multiple molecular linkages. The relationship between the characteristics of these linkages, the resulting interpatch connectivity, and assembly morphology is not well-explored. Here, we investigate assembly behavior of model divalent nanomonomers, DNA nanocuboid with tailorable multilinking bonds. Our study reveals that the characteristics of individual molecular linkages and their collective properties have a profound effect on nanomonomer reactivity and resulting morphologies. Beyond linear nanopolymers, a common signature of divalent nanomonomers, we observe an effective valence increase as linkages lengthened, leading to the nanopolymer bundling. The experimental findings are rationalized by molecular dynamics simulations.

PDADMAC/PSS Oligoelectrolyte Multilayers: Internal Structure and Hydration Properties at Early Growth Stages from Atomistic Simulations.

We analyze the internal structure and hydration properties of poly(diallyl dimethyl ammonium chloride)/poly(styrene sulfonate sodium salt) oligoelectrolyte multilayers at early stages of their layer-by-layer growth process. Our study is based on large-scale molecular dynamics simulations with atomistic resolution that we presented recently [Sánchez et al., Soft Matter 2019, 15, 9437], in which we produced the first four deposition cycles of a multilayer obtained by alternate exposure of a flat silica substrate to aqueous electrolyte solutions of such polymers at 0.1M of NaCl. In contrast to any previous work, here we perform a local structural analysis that allows us to determine the dependence of the multilayer properties on the distance to the substrate. We prove that the large accumulation of water and ions next to the substrate observed in previous overall measurements actually decreases the degree of intrinsic charge compensation, but this remains as the main mechanism within the interface region. We show that the range of influence of the substrate reaches approximately 3 nm, whereas the structure of the outer region is rather independent from the position. This detailed characterization is essential for the development of accurate mesoscale models able to reach length and time scales of technological interest.

Atomistic simulation of PDADMAC/PSS oligoelectrolyte multilayers: overall comparison of tri- and tetra-layer systems.

By employing large-scale molecular dynamics simulations of atomistically resolved oligoelectrolytes in aqueous solutions, we study in detail the first four layer-by-layer deposition cycles of an oligoelectrolyte multilayer made of poly(diallyl dimethyl ammonium chloride)/poly(styrene sulfonate sodium salt) (PDADMAC/PSS). The multilayers are grown on a silica substrate in 0.1 M NaCl electrolyte solutions and the swollen structures are then subsequently exposed to varying added salt concentration. We investigated the microscopic properties of the films, analyzing in detail the differences between three- and four-layer systems. Our simulations provide insights into the early stages of growth of a multilayer, which are particularly challenging for experimental observations. We found rather strong complexation of the oligoelectrolytes, with fuzzy layering of the film structure. The main charge compensation mechanism is for all cases intrinsic, whereas extrinsic compensation is relatively enhanced for the layer of the last deposition cycle. In addition, we quantified other fundamental observables of these systems, such as the film thickness, water uptake, and overcharge fractions for each deposition layer.

Modeling the magnetostriction effect in elastomers with magnetically soft and hard particles.

We analyze theoretically the field-induced microstructural deformations in a hybrid elastomer, that consists of a polymer matrix filled with a mixture of magnetically soft and magnetically hard spherical microparticles. These composites were introduced recently in order to obtain a material that allows the tuning of its properties by both, magnetically active and passive control. Our theoretical analysis puts forward two complementary models: a continuum magnetomechanical model and a bead-spring computer simulation model. We use both approaches to describe qualitatively the microstructural response of such elastomers to applied external fields, showing that the combination of magnetically soft and hard particles may lead to an unusual magnetostriction effect: either an elongation or a shrinking in the direction of the applied field depending on its magnitude. This behavior is observed for conditions (moderate particle densities, fields and deformations) under which the approximations of our models (linear response regime, negligible mutual magnetization between magnetically soft particles) are physically valid.

Surface relief of magnetoactive elastomeric films in a homogeneous magnetic field: molecular dynamics simulations.

The structure of a thin magnetoactive elastomeric (MAE) film adsorbed on a solid substrate is studied by molecular dynamics simulations. Within the adopted coarse-grained approach, a MAE film consists of magnetic particles modeled as soft-core spheres, carrying point dipoles, connected by elastic springs representing a polymer matrix. MAE films containing 20, 25 and 30 vol% of randomly distributed magnetic particles are simulated. Once a magnetic field is applied, the competition between dipolar, elastic and Zeeman forces leads to the restructuring of the layer. The distribution of the magnetic particles as well as elastic strains within the MAE films are calculated for various magnetic fields applied perpendicular to the film surface. It is shown that the surface roughness increases strongly with growing magnetic field. For a given magnetic field, the roughness is larger for the softer polymeric matrix and exhibits a nonmonotonic dependence on the magnetic particle concentration. The obtained results provide a better understanding of the MAE surface structuring as well as possible guidelines for fabrication of MAE films with a tunable surface topology.

Magnetic responsive brushes under flow in strongly confined slits: external field control of brush structure and flowing particle mixture separation.

In the present work magnetic brushes under flow conditions and confined inside narrow slits have been studied using Langevin dynamics simulations. It has been observed that the structural properties of these confined magnetic brushes can be tuned via the application of an external magnetic field, and this control can be exerted with a relatively low content of magnetic colloidal particles in the filaments that form the brushes (20% in the present study). The potential of these brushes to perform a separation process of a size-bidispersed mixture of free non-magnetic colloidal particles flowing through the slit has also been explored. Numerical results show that it is possible to induce a two-fold effect on the bidispersed particle flow: a lateral separation of the two types of flowing colloidal particles and an enhancement of the differences in their velocities. These two features are key elements sought in separation processes and could be very relevant in the design of new chromatographic columns and microfluid separation devices.

The structure of clusters formed by Stockmayer supracolloidal magnetic polymers.

Unlike Stockmayer fluids, that prove to undergo gas-liquid transition on cooling, the system of dipolar hard or soft spheres without any additional central attraction so far has not been shown to have a critical point. Instead, in the latter, one observes diverse self-assembly scenarios. Crosslinking dipolar soft spheres into supracolloidal magnetic polymer-like structures (SMPs) changes the self-assembly behaviour. Moreover, aggregation in systems of SMPs strongly depends on the constituent topology. For Y- and X-shaped SMPs, under the same conditions in which dipolar hard spheres would form chains, the formation of very large loose gel-like clusters was observed (E. Novak et al., J. Mol. Liq. 271, 631 (2018)). In this work, using molecular dynamics simulations, we investigate the self-assembly in suspensions of four topologically different SMPs --chains, rings, X and Y-- whose monomers interact via Stockmayer potential. As expected, compact drop-like clusters are formed by SMPs in all cases if the central isotropic attraction is introduced, however, their shape and internal structure turn out to depend on the SMPs topology.

Importance of matrix inelastic deformations in the initial response of magnetic elastomers.

Being able to predict and understand the behaviour of soft magnetic materials paves the way to their technological applications. In this study we analyse the magnetic response of soft magnetic elastomers (SMEs) with magnetically hard particles. We present experimental evidence of a difference between the first and next magnetisation loops exhibited by these SMEs, which depends non-monotonically on the interplay between the rigidity of the polymer matrix, its mechanical coupling with the particles, and the magnetic interactions in the system. In order to explain the microstructural mechanism behind this behaviour, we used a minimal computer simulation model whose results evidence the importance of irreversible matrix deformations due to both translations and rotations of the particles. To confirm the simulation findings, computed tomography (CT) was used. We conclude that the initial exposure to the field triggers the inelastic matrix relaxation in the SMEs, as particles attempt to reorient. However, once the necessary degree of freedom is achieved, both the rotations and the magnetisation behaviour become stationary. We expect this scenario not only to be limited to the materials studied here, but also to apply to a broader class of hybrid SMEs.

Coarsening dynamics of ferromagnetic granular networks-experimental results and simulations.

We investigate the phase separation of a shaken mixture of glass and magnetised steel spheres after a sudden quench of the shaker amplitude. After quenching, transient networks of steel spheres emerge in the experiment. For the developing network clusters we estimate the number of spheres in them, and the characteristic path lengths. We find that both quantities follow a log-normal distribution function. Moreover, we study the temporal evolution of the networks. In the sequence of snapshots we observe an initial regime, where the network incubates, followed by a temporal regime where network structures are elongated and broken, and finally a regime where the structures have relaxed to compact clusters of rounded shapes. This phaenomenology resembles the initial, elastic and hydrodynamic regimes observed by H. Tanaka [J. Phys.: Condens. Matter, 2000, 12, R207] during the viscoelastic phase separation for dynamically asymmetric mixtures of polymers. In order to discriminate the three regimes we investigate in the experiment order parameters like the mean number of neighbors and the efficiency of the networks. In order to capture the origin for a viscoelastic phase separation in our granular mixture, we use a simple simulation approach. Not aiming at a quantitative description of the experimental results, we rather use the simulations to define the key interactions in the experimental system. This way, we discover that along with dipolar and steric interactions, there is an effective central attraction between the magnetised spheres that is responsible for the coarsening dynamics. Our simulations show as well three regimes in the evolution of characteristic order parameters.

Scattering properties and internal structure of magnetic filament brushes.

Practical applications of polymer brush-like systems rely on a clear understanding of their internal structure. In the case of magnetic nanoparticle filament brushes, the competition between bonding and nonbonding interactions-including long range magnetic dipole-dipole interactions-makes the microstructure of these polymer brush-like systems rather complex. On the other hand, the same interactions open up the possibility to manipulate the meso- and macroscopic responses of these systems by applying external magnetic fields or by changing the background temperature. In this study, we put forward an approach to extract information about the internal structure of a magnetic filament brush from scattering experiments. Our method is based on the mapping of the scattering profiles to the information about the internal equilibrium configurations of the brushes obtained from computer simulations. We show that the structure of the magnetic filament brush is strongly anisotropic in the direction perpendicular to the grafting surface, especially at low temperatures and external fields. This makes slice-by-slice scattering measurements a technique very useful for the study of such systems.

Magnetic filament brushes: tuning the properties of a magnetoresponsive supracolloidal coating.

We present a theoretical study on the design of a supramolecular magnetoresponsive coating. The coating is formed by a relatively dense array of supracolloidal magnetic filaments grafted to a surface in a polymer brush-like arrangement. In order to determine and optimise the properties of the magnetic filament brush, we perform extensive computer simulations with a coarse-grained model that takes into account the correlations between the magnetic moments of the particles and the backbone crosslinks. We show that the self-assembly of magnetic beads from neighbouring filaments defines the equilibrium structural properties of the complete brush. In order to control this self-assembly, we highlight two external stimuli that can lead to significant effects: temperature of the system and an externally applied magnetic field. Our study reveals self-assembly scenarios inherently driven by the crosslinking and grafting constraints. Finally, we explain the mechanisms of structural changeovers in the magnetic filament brushes and confirm the possibility of controlling them by changing the temperature or the intensity of an external magnetic field.

Flexible magnetic filaments under the influence of external magnetic fields in the limit of infinite dilution.

In the present work we use Langevin dynamics computer simulations to understand how the presence of a constant external magnetic field modifies the conformational phase diagram of magnetic filaments in the limit of infinite dilution. We have considered the filaments immersed in either a good (non-sticky filaments) or a poor (Stockmayer polymers) solvent. It has been found that in the presence of an applied field, filaments turn out to be much more susceptible to parameters such as temperature and solvent conditions. Filaments owe this increased susceptibility to the fact that the external magnetic field tends to level the free energy landscape as compared to the zero-field case. The field induces equalization in the free energy of competing conformational states that were separated by large energy differences in the zero-field limit. In this new scenario multistability arises, and manifests itself in the existence of broad regions in the phase diagram where two or more equilibrium configurations coexist. The existence of multistability greatly enhances the possibility of tuning the properties of the filament.

Monitoring the world's agriculture.

To feed the world without further damaging the planet, Jeffrey Sachs and 24 foodsystem experts call for a global data collection and dissemination network to track the myriad impacts of different farming practices.

The behavior of a magnetic filament in flow under the influence of an external magnetic field.

We present an extensive numerical study of the behaviour of a filament made of ferromagnetic colloidal particles subjected to the simultaneous action of a fluid flow and a stationary external magnetic field perpendicular to the flow lines. We found that in the presence of a shear flow, the tumbling motion observed at zero field is strongly inhibited when the external magnetic field is applied. The field is able to stabilise the filament with a well defined degree of alignment that depends on the balance between hydrodynamic and magnetic torques. In addition, for a Poiseuille flow, it has been found that the initial position has a long lasting influence on the behaviour of the magnetic filament when the external field is applied.

The effect of links on the interparticle dipolar correlations in supramolecular magnetic filaments.

We present a combined computational and analytical study of supramolecular magnetic filaments, i.e., permanently linked chains of ferromagnetic nanocolloids. We put forward two different models for the interparticle connectivity within the chain. In the first model, the magnetic dipoles of the particles are free to rotate independently from the permanent links. The second model penalises the misalignment of the dipoles by coupling their orientations to the chain backbone. We show that the effect of the long-range magnetic dipolar interactions on the zero field net magnetic moment of the chain becomes less significant in the second case. However, the overall magnetic response in the model of freely rotating dipoles is much weaker.

Dynamic response of a ferromagnetic nanofilament under rotating fields: effects of flexibility, thermal fluctuations and hydrodynamics.

Using nonequilibrium computer simulations, we study the response of ferromagnetic nanofilaments, consisting of stabilized one dimensional chains of ferromagnetic nanoparticles, under external rotating magnetic fields. In difference with their analogous microscale and stiff counterparts, which have been actively studied in recent years, nonequilibrium properties of rather flexible nanoparticle filaments remain mostly unexplored. By progressively increasing the modeling details, we are able to evidence the qualitative impact of main interactions that can not be neglected at the nanoscale, showing that filament flexibility, thermal fluctuations and hydrodynamic interactions contribute independently to broaden the range of synchronous frequency response in this system. Furthermore, we also show the existence of a limited set of characteristic dynamic filament configurations and discuss in detail the asynchronous response, which at finite temperature becomes probabilistic.

Assessing the relevance of mental health factors in fibromyalgia severity: A data-driven case study using explainable AI.

BACKGROUND AND OBJECTIVE: Fibromyalgia is a chronic disease that causes pain and affects patients' quality of life. Current treatments focus on pharmacological therapies for pain reduction. However, patients' psychological well-being is also affected, with depression and pain catastrophizing being common. This research addresses the clinicians' need to assess the influence of mental health factors on FM severity compared to pain factors. METHODS: A co-development study between FM clinicians and data scientists analyzed data from 166 FM-diagnosed patients to assess the influence of mental health factors on FM severity in comparison to pain factors. The study used the Polysymptomatic Distress Scale (PDS) and Fibromyalgia Impact Questionnaire (FIQ) as FM severity indicators and collected 15 variables including regarding demographics, pain intensity perceived, and mental health factors. The team used an author's developed framework to identify the optimal FM severity classifier and explainability by selecting a number of features that lead to obtaining the best classification result. Machine learning classifiers employed in the framework were: decision trees, logistic regression, support vector machines, random forests, AdaBoost, extra trees, and RUSBoost. Explainability analyses were conducted using the following explainable AI techniques: SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Mean Decrease Impurity (MDI). RESULTS: A balanced random forest with 6 features achieved the best performance with PDS (AUC_ROC, mean = 0.81, std = 0.07). Being FIQ the target variable, due to the imbalance in FM severity levels, a binary and a multiclass classification approaches were considered achieving the optimal performance, respectively, a logistic regression classifier (AUC_ROC, mean = 0.83, std = 0.08) with 6 selected features, and a random forest (AUC_ROC, mean = 0.91, std = 0.04) with 8 selected features. Next, the explainability analysis determined mental health factors were found to be more relevant than pain perceived factors for FM severity. CONCLUSIONS: This study's findings, validated by clinicians, are potentially aligned with FM international guidelines that promote non-pharmacological interventions such as promoting mental well-being of FM patients.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review.

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

Effects of the dipolar interaction on the equilibrium morphologies of a single supramolecular magnetic filament in bulk.

We study the equilibrium morphologies of a single supramolecular magnetic filament in a three-dimensional system as a function of the effective strength of the magnetic dipolar interactions. The study is performed by means of Langevin dynamics simulations with a bead-spring chain model of freely rotating dipoles. We demonstrate the existence of three structural regimes as the value of the dipolar coupling parameter is increased: a coil compaction regime, a coil expansion regime, and a closed chain regime in which the structures tend progressively to an ideal ring configuration. We discuss the governing effects of each regime, the structural transition between open and closed morphologies, and the reasons why we see no multiloop configurations that have been observed in two-dimensional systems under similar conditions.

Improvement of a prediction model for heart failure survival through explainable artificial intelligence.

Cardiovascular diseases and their associated disorder of heart failure (HF) are major causes of death globally, making it a priority for doctors to detect and predict their onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnoses and treatments. Specifically, "eXplainable AI" (XAI) offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of two HF survival prediction models using a dataset that includes 299 patients who have experienced HF. The first model utilizes survival analysis, considering death events and time as target features, while the second model approaches the problem as a classification task to predict death. The model employs an optimization data workflow pipeline capable of selecting the best machine learning algorithm as well as the optimal collection of features. Moreover, different post hoc techniques have been used for the explainability analysis of the model. The main contribution of this paper is an explainability-driven approach to select the best HF survival prediction model balancing prediction performance and explainability. Therefore, the most balanced explainable prediction models are Survival Gradient Boosting model for the survival analysis and Random Forest for the classification approach with a c-index of 0.714 and balanced accuracy of 0.74 (std 0.03) respectively. The selection of features by the SCI-XAI in the two models is similar where "serum_creatinine", "ejection_fraction", and "sex" are selected in both approaches, with the addition of "diabetes" for the survival analysis model. Moreover, the application of post hoc XAI techniques also confirm common findings from both approaches by placing the "serum_creatinine" as the most relevant feature for the predicted outcome, followed by "ejection_fraction". The explainable prediction models for HF survival presented in this paper would improve the further adoption of clinical prediction models by providing doctors with insights to better understand the reasoning behind usually "black-box" AI clinical solutions and make more reasonable and data-driven decisions.