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.

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.

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.

Controlling the coarsening dynamics of ferrogranular networks by means of a vertical magnetic field.

We are exploring in experiments the aggregation process in a shaken granular mixture of glass and magnetized steel beads, filled in a horizontal vessel, after the shaking amplitude is suddenly decreased. Then the magnetized beads form a transient network that coarsens in time into compact clusters, resembling a viscoelastic phase separation [Tanaka, J. Phys.: Condens. Matter 12, R207 (2000)0953-898410.1088/0953-8984/12/15/201], where attached beads represent the slow phase. Here we investigate how a homogeneous magnetic field oriented in vertical direction impedes the emergence and growth of the networks. With increasing field amplitude this phase is replaced by a fluctuating arrangement of repelling, isolated steel beads. The experimental results are compared with those of computer simulations. Coarse-grained molecular dynamics confirms the impact of an applied magnetic field on the structural transitions and allows us to investigate long-time regimes and magnetic response not yet accessible in the experiment. It turns out that an applied magnetic field has different impacts, depending on it strength. It can be used either to slow down the dynamics of the structural transitions without changing the type of the resulting phases and only affecting the amount and sizes of clusters, or to fully impede the formation of network-like and compact aggregates of steel beads.

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.

Circular agriculture increases food production and can reduce N fertilizer use of commercial farms for tropical environments.

World food production must increase in the coming years with minimal environmental impact for food and nutrition security. Circular Agriculture has emerged as an approach to minimize non-renewable resource depletion and encourage by-product reuse. The goal of this study was to evaluate Circular Agriculture as a tool to increase food production and N recovery. The assessment was conducted for two Brazilian farms (Farm 1; Farm 2) with Oxisols under no-till and a diversified cropping system, including five species of grain, three cover crop species, and sweet potato. Both farms implemented an annual two-crop rotation and an integrated crop-livestock system with beef cattle confined for 2-years. Grain and forage from the fields, leftovers from silos, and crop residues were used as cattle feed. Grain yield was 4.8 and 4.5 t ha-1 for soybean, 12.5 and 12.1 t ha-1 for maize, and 2.6 and 2.4 t ha-1 for common bean, for Farm 1 and Farm 2, respectively, which is higher than the national average. The animals gained 1.2 kg day-1 of live weight. Farm 1 exported 246 kg ha-1 year-1 of N in grains, tubers, and animals, while 216 kg ha-1 year-1 was added as fertilizer and N to cattle. Farm 2 exported 224 kg ha-1 year-1 in grain and animals, while 215 kg ha-1 year-1 was added as fertilizer and N to cattle. Circular practices, i.e., no-till, crop rotation, year-round soil covered, maize intercropped with brachiaria ruziziensis, biological N fixation, and crop-livestock integration, increased crop yield and decreased N application by 14.7 % (Farm 1) and 4.3 % (Farm 2). 85 % of the N consumed by the confined animals was excreted and converted into organic compost. Overall, circular practices associated with adequate crop management allowed recovering high rate of applied N, reducing environmental impacts, and increasing food production with lower production costs.

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.

Evaluation of Machine Learning Algorithms and Explainability Techniques to Detect Hearing Loss From a Speech-in-Noise Screening Test.

PURPOSE: The aim of this study was to analyze the performance of multivariate machine learning (ML) models applied to a speech-in-noise hearing screening test and investigate the contribution of the measured features toward hearing loss detection using explainability techniques. METHOD: Seven different ML techniques, including transparent (i.e., decision tree and logistic regression) and opaque (e.g., random forest) models, were trained and evaluated on a data set including 215 tested ears (99 with hearing loss of mild degree or higher and 116 with no hearing loss). Post hoc explainability techniques were applied to highlight the role of each feature in predicting hearing loss. RESULTS: Random forest (accuracy = .85, sensitivity = .86, specificity = .85, precision = .84) performed, on average, better than decision tree (accuracy = .82, sensitivity = .84, specificity = .80, precision = .79). Support vector machine, logistic regression, and gradient boosting had similar performance as random forest. According to post hoc explainability analysis on models generated using random forest, the features with the highest relevance in predicting hearing loss were age, number and percentage of correct responses, and average reaction time, whereas the total test time had the lowest relevance. CONCLUSIONS: This study demonstrates that a multivariate approach can help detect hearing loss with satisfactory performance. Further research on a bigger sample and using more complex ML algorithms and explainability techniques is needed to fully investigate the role of input features (including additional features such as risk factors and individual responses to low-/high-frequency stimuli) in predicting hearing loss.

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.

A Social Network Analysis of Tweets Related to Masks during the COVID-19 Pandemic.

BACKGROUND: High compliance in wearing a mask is a crucial factor for stopping the transmission of COVID-19. Since the beginning of the pandemic, social media has been a key communication channel for citizens. This study focused on analyzing content from Twitter related to masks during the COVID-19 pandemic. METHODS: Twitter data were collected using the keyword "mask" from 27 June 2020 to 4 July 2020. The total number of tweets gathered were n = 452,430. A systematic random sample of 1% (n = 4525) of tweets was analyzed using social network analysis. NodeXL (Social Media Research Foundation, California, CA, USA) was used to identify users ranked influential by betweenness centrality and was used to identify key hashtags and content. RESULTS: The overall shape of the network resembled a community network because there was a range of users conversing amongst each other in different clusters. It was found that a range of accounts were influential and/or mentioned within the network. These ranged from ordinary citizens, politicians, and popular culture figures. The most common theme and popular hashtags to emerge from the data encouraged the public to wear masks. CONCLUSION: Towards the end of June 2020, Twitter was utilized by the public to encourage others to wear masks and discussions around masks included a wide range of users.

Adsorption transition of a grafted ferromagnetic filament controlled by external magnetic fields.

Extensive Langevin dynamics simulations are used to characterize the adsorption transition of a flexible magnetic filament grafted onto an attractive planar surface. Our results identify different structural transitions at different ratios of the thermal energy to the surface attraction strength: filament straightening, adsorption, and the magnetic flux closure. The adsorption temperature of a magnetic filament is found to be higher in comparison to an equivalent nonmagnetic chain. The adsorption has been also investigated under the application of a static homogeneous external magnetic field. We found that the strength and the orientation of the field can be used to control the adsorption process, providing a precise switching mechanism. Interestingly, we have observed that the characteristic field strength and tilt angle at the adsorption point are related by a simple power law.

Directing the Diffusion of a Nonmagnetic Nanosized Active Particle with External Magnetic Fields.

With the help of molecular dynamics simulations we show that an arbitrary nonmagnetic active particle with a size below one micrometer, being immersed in a suspension of magnetic nanoparticles, can diffuse faster along the direction of an applied field than perpendicular to the latter. This effect is demonstrated in monodisperse and polydisperse systems of magnetic nanoparticles for magnetic fields of moderate strength. The ability to direct a nonmagnetic active particle along the magnetic field stems from the formation of chains of magnetic nanoparticles aligned with the field direction. Such chains form effective channels through which the active particle can diffuse. We combine the investigations of the diffusion and transport efficiency of the active particle parallel and perpendicular to the field with the structural analysis of the magnetic nanoparticle system and find that the ability to direct an active particle of a given size can be maximized by changing magnetic particle concentration. The optimal transport efficiency is achieved at a concentration of magnetic material that provides a mean width of the effective tunnels that matches the effective size of the active particle.

Monitoring and Intervention Technologies to Manage Diabetic Older Persons: The CAPACITY Case-A Pilot Study.

Diabetes Mellitus is a chronic disease with a high prevalence among older people, and it is related to an increased risk of functional and cognitive decline, in addition to classic micro and macrovascular disease and a moderate increase in the risk of death. Technology aimed to improve elder care and quality of life needs to focus in the early detection of decline, monitoring the functional evolution of the individuals and providing ways to foster physical activity, to recommend adequate nutritional habits and to control polypharmacy. But apart from all these core features, some other elements or modules covering disease-specific needs should be added to complement care. In the case of diabetes these functionalities could include control mechanisms for blood glucose and cardiovascular risk factors, specific nutritional recommendations, suited physical activity programs, diabetes-specific educational contents, and self-care recommendations. This research work focuses on those core aspects of the technology, leaving out disease-specific modules. These central technological components have been developed within the scope of two research and innovation projects (FACET and POSITIVE, funded by the EIT-Health), that revolve around the provision of integrated, continuous and coordinated care to frail older population, who are at a high risk of functional decline. Obtained results indicate that a geriatric multimodal intervention is effective for preventing functional decline and for reducing the use of healthcare resources if administered to diabetic pre-frail and frail older persons. And if such intervention is supported by the CAPACITY technological ecosystem, it becomes more efficient.

Correction: Characterisation of the magnetic response of nanoscale magnetic filaments in applied fields.

Correction for 'Characterisation of the magnetic response of nanoscale magnetic filaments in applied fields' by Deniz Mostarac et al., Nanoscale, 2020, DOI: .

Characterisation of the magnetic response of nanoscale magnetic filaments in applied fields.

Incorporating magnetic nanoparticles (MNPs) within permanently crosslinked polymer-like structures opens up the possibility for synthesis of complex, highly magneto-responsive systems. Among such structures are chains of prealigned magnetic (ferro- or super-paramagnetic) monomers, permanently crosslinked by means of macromolecules, which we refer to as magnetic filaments (MFs). In this paper, using molecular dynamics simulations, we encompass filament synthesis scenarios, with a compact set of easily tuneable computational models, where we consider two distinct crosslinking approaches, for both ferromagnetic and super-paramagnetic monomers. We characterise the equilibrium structure, correlations and magnetic properties of MFs in static magnetic fields. Calculations show that MFs with ferromagnetic MNPs in crosslinking scenarios where the dipole moment orientations are decoupled from the filament backbone, have similar properties to MFs with super-paramagnetic monomers. At the same time, magnetic properties of MFs with ferromagnetic MNPs are more dependent on the crosslinking approach than they are for ones with super-paramagnetic monomers. Our results show that, in a strong applied field, MFs with super-paramagnetic MNPs have similar magnetic properties to ferromagnetic ones, while exhibiting higher susceptibility in low fields. We find that MFs with super-paramagnetic MNPs have a tendency to bend the backbone locally rather than to fully stretch along the field. We explain this behaviour by supplementing Flory theory with an explicit dipole-dipole interaction potential, with which we can take in to account folded filament configurations. It turns out that the entropy gain obtained through bending compensates an insignificant loss in dipolar energy for the filament lengths considered in the manuscript.

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.

FriWalk robotic walker: usability, acceptance and UX evaluation after a pilot study in a real environment.

Background: Scientific evidence supports that prevention strategies like multicomponent physical exercise help avoiding functional decline, falls and frailty. The robotic walker FriWalk, developed within the ACANTO project, supports the execution of controlled physical activities during hospital admission to prevent functional deterioration associated to prolonged bedrest. FriWalk shows in a clinical validation study a positive relationship with improvement in physical performance, basic activities of daily living execution and frailty status. Usability, acceptance and user experience (UX) are key aspects to ease the adoption of assistive technologies in the elderly.Objective: This work pursues the evaluation of the usability, acceptance and UX of the FriWalk from the patients and clinical professionals' perspectives.Methods: Data collected during the validation of FriWalk in a real environment have been used. Forty-two patients recruited at Getafe University Hospital (Acute Care and Orthogeriatric Units) and one clinical professional participated. SUS, TAM, UX and ad hoc questionnaires were administered.Results: Patients provided an average SUS of 52.86 and provided valuable information in the qualitative acceptance interviews. The clinical professional provided an averaged SUS and TAM of 67 and 46.6, respectively, and evaluated all UX categories as above average.Conclusions: Usability results do not qualify FriWalk as above average; the reasons explaining this have been identified and point out to the prototypical stage of the hardware. Acceptance and UX were positively evaluated and allowed the research team to propose a new organizational model to deliver the FriWalk-based prevention program. FriWalk will be soon evolved.Implications for rehabilitationFriWalk showed in a randomized clinical trial a positive relationship with improvement in physical performance, basic activities of daily living execution and frailty status.In terms of usability, user experience (UX) and acceptance, participants of the study have valued the FriWalk robotic walker as a promising help, considering that the device that has been under evaluation was still in a prototype stage.Clinical professional reported FriWalk and its corresponding exercise program description software regarding usability, acceptance and UX as satisfactory tool to prescribe and assess a rehabilitation program for hospitalized patients.

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.

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.