2063 research outputs found
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Vaccination strategies in a pair formation model for human papillomavirus infection: An optimal control approach
No full textHuman papillomavirus (HPV) infection is a widespread sexually transmitted infection responsible for several cancers including anal, oropharyngeal, penile, vaginal, and cervical cancer. Despite HPV vaccines have been available for almost 20 years and are incredibly effective in preventing infection, the scale-up of vaccination has been slow in many low and middle-income countries. This analysis uses a pair model that explicitly accounts for sexual partnership formation to investigate HPV immunization programs. The optimality of vaccine interventions is analyzed using optimal control theory. We give formal proof of the existence of optimal control solutions and obtain first-order optimality conditions via Pontryagin's Maximum Principle. Extensive numerical simulations are used to investigate plausible what-if scenarios to understand under which conditions the inclusion of males should be recommended in addition to female vaccination. The results suggest that a gender-neutral vaccination program should be recommended in regions where vaccination uptake in women is still low whereas for an already existing female-only program with high uptake, it is more effective to keep increasing coverage in females
Optimising performance of Hamiltonian Monte Carlo (HMC) in molecular simulation and computational statistics
Full text linkTechnological advancements have led to increasingly complex systems, demanding sophisticated computational methods for understanding phenomena, making predictions, and informing decisions. In this context, the efficiency of Bayesian probabilistic modeling becomes a critical factor.
Hamiltonian/Hybrid Monte Carlo (HMC) has emerged as a leading Markov Chain Monte Carlo (MCMC) method in both molecular simulations and computational statistics applications. By combining strengths of Metropolis-Hastings algorithm and Hamiltonian
dynamics, HMC circumvents the inefficient random walk behavior of traditional MCMC methods, achieving faster convergence to stationarity without compromising computational tractability.
Despite these benefits, sampling efficiency and computational performance of HMC heavily depend on a choice of a numerical integrator and intrinsic hyperparameters, often selected heuristically, through trial-and-error, or via costly optimization.
This thesis addresses such limitations by introducing novel adaptive, automated, computationally efficient strategies for enhancing performance of HMC-based methods. In particular, we develop adaptive integration approaches that leverage multivariate
Gaussian analysis and available HMC simulation data to detect the most favorable multi-stage splitting integrator for maximizing sampling efficiency of an HMC simulation at an arbitrary step size within the predicted dimensional stability interval. Our integration
strategies are designed to be robust across applications in both molecular simulation and computational statistics, addressing the distinct challenges posed by each domain. In addition, for molecular simulation, we derived new multiple time-step multi-stage splitting integrators to offer an effective approach for integrating molecular systems with complex
time-scale separations.
Further, we introduce an adaptive, computationally inexpensive procedure for tuning hyperparameters of HMC and Generalized HMC (GHMC) in Bayesian inference applications. Given a probabilistic model and observed data, our tuning algorithm identifies
optimal values for the integration step size, the number of integration steps per iteration, and the noise level in momentum refreshment for GHMC, along with optimum widths of their randomization intervals.
Our novel methodologies are implemented in HaiCS and MultiHMC-GROMACS, in-house software packages developed at BCAM for Bayesian inference and molecular simulation, respectively, and can be easily adapted to any HMC-based package in these fields.
Numerical experiments on benchmark statistical models, molecular systems, and real-world case studies demonstrate significant performance improvements (up to two orders of magnitude) in comparison with the state-of-the-art numerical integrators, alternative
choices of HMC/GHMC hyperparameter settings, and optimized samplers
Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge
Full text linkContinuous cough monitors can greatly benefit doctors in home monitoring and treatment of respiratory diseases. Although many works propose algorithms to automate this task, they suffer of poor data privacy and short-term monitoring. Edge-AI is a promising paradigm to overcome these limitations by processing privacy-sensitive data close to their source. However, it presents challenges for the deployment of resource-demanding algorithms on constrained devices. In this work, we propose a hardware-aware methodology for developing a cough detection algorithm, analyzing design-time trade-offs for performance and energy. From audio and kinematic signals, our methodology aims at optimal features via Recursive Feature Elimination with Cross-Validation (RFECV), exploiting the explainability of the selected XGB model. Additionally, it analyzes the use of Mel spectrogram features, instead of the common MFCC. Moreover, a set of hyperparameters for a multimodal implementation of the classifier is explored. Finally, it evaluates the performance based on clinically relevant event-based metrics. The methodology proposes a novel structured approach to efficiently deploy AI on the edge, preserving data privacy. We apply our methodology to develop Cough-E, an energy-efficient, multimodal, and edge AI cough detector. It exploits audio and kinematic data in two distinct models, cooperating for a balanced energy and performance trade-off. We demonstrate that our algorithm can be executed in real-time on an ARM Cortex M33 microcontroller. Cough-E achieves a 70.56% energy saving compared to the audio-only approach, for a 1.26% relative performance drop, resulting in a 0.78 F1-score. Both Cough-E and the edge-aware model optimization methodology are available as open-source code. This approach demonstrates the benefits of the proposed hardware-aware methodology to enable privacy-preserving cough monitors on the edge, paving the way to efficient cough monitoring.RYC2021-032853-
Evolutionary convergence of sensory circuits in the pallium of amniotes
No full textThe amniote pallium contains sensory circuits that are structurally and functionally equivalent, yet their evolutionary relationship remains unresolved. We used birthdating analysis, single-cell RNA and spatial transcriptomics, and mathematical modeling to compare the development and evolution of known pallial circuits across birds (chick), lizards (gecko), and mammals (mouse). We reveal that neurons within these circuits’ stations are generated at varying developmental times and brain regions across species and found an early developmental divergence in the transcriptomic progression of glutamatergic neurons. Our research highlights developmental distinctions and functional similarities in the sensory circuit between birds and mammals, suggesting the convergence of high-order sensory processing across amniote lineages
Collocation-based robust variational physics-informed neural networks (CRVPINNs)
Full text linkPhysics-informed neural networks (PINNs) have been widely used to solve partial differential equations (PDEs) through strong residual minimization formulations. Their extension to weak scenarios via Variational PINNs (VPINNs) has been shown to lack robustness when the discrete and continuous-level norms are mismatched. Robust Variational PINNs (RVPINNs) address this problem by appropriately incorporating the Gram matrix but suffer from high computational costs due to the weak residual integration and the Gram matrix inversion. In this work, we accelerate RVPINN computations by using a point-collocation approach similar to PINNs, and by employing an LU factorization of the sparse Gram matrix. This leads to the proposed Collocation-Based Robust Variational PINN (CRVPINN). We validate CRVPINN on Laplace, advection–diffusion, Stokes, non-linear stationary Navier–Stokes, and linear elasticity problems in two spatial dimensions, demonstrating improved efficiency without compromising robustness
On design, analysis, and hybrid manufacturing of microstructured blade-like geometries
No full textWith the evolution of new manufacturing technologies such as multi-material 3D printing, one can think of new type of objects that consist of considerably less, yet heterogeneous, material, consequently being porous, lighter and cheaper, while having the very same functionality as the original object when manufactured from one single solid material. We aim at questioning five decades of traditional paradigms in geometric CAD and focus at new generation of CAD objects that are not solid, but contain heterogeneous free-form internal microstructures. We propose a unified manufacturing pipeline that involves all stages, namely design, optimization, manufacturing, and inspection of microstructured free-form geometries. We demonstrate our pipeline on an industrial test case of a blisk blade that sustains the desired pressure limits, yet requires significantly less material when compared to the solid counterpart
Efficient Light to Heat Conversion in Sb2Se3 Nanorods and the Role of Macro-channel Imprinted Sb2Se3 Loaded Hybrid Membrane for Superior Desalination Performance
No full textThis report based on theory and experiment validates Sb2Se3 nanorods (NRs) as a potential contender for phototransduction heat generation. Customized water droplet experiment leads to the evaluation of promising light-to-heat conversion efficiencies of Sb2Se3 which are approximately 57.8 and 58% for red (671 nm) and green (532 nm) laser respectively. Following this we designed PVDF(M)/ Sb2Se3 NRs hybrid membranes for solar desalination which eventually generates about 59 ℃ temperature after 15 minutes of illumination. The heat generation is dominated by electron/hole- acoustic phonon scattering mechanism. In spite of having superior visible-NIR absorption and heat localization in Sb2Se3 NRs, the hybrid membranes show evaporation rate 1.72 kg m-2 h-1 only, even if mass loading is increased. The hydrophobic Sb2Se3 NRs layer limits the uniform diffusion of water to the hot zones and hence limits the phototransduction efficiency. To remove the procrastination of water transport to the hot zone, we establish a novel strategy of mechanically imprinting macro-channel in the hybrid membranes which thereby eliminates the delay of water transport. Consequently, the optimized macro-channel membranes show elevated mass loss approximately 2.37 kg m-2 h-1 with phototransduction efficiency 148% under mercury vapor lamp of intensity 1000 W m-2. Therefore, imprinting macro-channel could be a possible strategy, addressing the hydrophobic materials in desalination application which could be expanded in other similar materials. Moreover, we extend our application in outdoor sunlight with which also shows commendable phototransduction efficiencies approximately138%. The steam produced in the solar thermal heat generation process effectively eliminates heavy metal ions, adhering to the health standards set by the World Health Organization (WHO) for potable water.JDC2022-049793-I/MCIN/AEI/10.13039/501100011033, RES, QHS-2023-2-003
On complex mode shapes and their identification
No full textIn experimental modal analysis, proportional damping is a convenient simplification that allows practitioners to fit their data to a model. However, this simplification seriously reduces the number of free parameters for fitting, and some systems may be misrepresented by the assumption of proportional damping. We discuss the mathematics behind nonproportional systems, and we propose methods to deal with the computationally demanding problem of fitting data with complex mode shapes.MICIU/AEI/10.13039/501100011033 and ERDF/UE, grant PLEC2024-011247.
MICIU/AEI/10.13039/501100011033 and ERDF/UE, grant PID2023-146640NB-I00
Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques
No full textBackground Mosquito‑borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region
of Spain, changing climatic conditions have driven the spread of invasive mosquitoes, increasing the potential
for local transmission of diseases such as dengue, Zika, and chikungunya. The establishment of mosquito species
in new areas, coupled with rising mosquito populations and viremic imported cases, presents challenges for public
health systems in non‑endemic regions.
Methods This study uses models that capture the complexities of the mosquito life cycle, driven by interactions
with weather variables, including temperature, precipitation, and humidity. Leveraging machine learning techniques,
we aimed to forecast Aedes invasive mosquito abundance in the provinces of the Basque Country, using egg count
as a proxy and weather features as key independent variables. A Spearman correlation was used to assess relationships between climate variables and mosquito egg counts, as well as their lagged time series versions. Forecasting
models, including random forest (RF) and seasonal autoregressive integrated moving average (SARIMAX), were evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics.
Results Statistical analysis revealed significant impacts of temperature, precipitation, and humidity on mosquito egg
abundance. The random forest (RF) model demonstrated the highest forecasting accuracy, followed by the SARIMAX
model. Incorporating lagged climate variables and ovitrap egg counts into the models improved predictions, enabling more accurate forecasts of Aedes invasive mosquito abundance.
Conclusions The findings emphasize the importance of integrating climate‑driven forecasting tools to predict
the abundance of mosquitoes where data are available. Furthermore, this study highlights the critical need for ongoing entomological surveillance to enhance mosquito spread forecasting and contribute to the development
and assessment of effective vector control strategies in regions of mosquito expansion.This work is also supported by the ARBOSKADI project for monitoring vector‑
borne diseases in the Basque Country, Euskadi. The collection of the data
was funded by the Department of Food, Rural Development, Agriculture and
Fisheries, and the Department of Health of the Basque Government, the Minis‑
try of Health, Social Policy, and Equality of the Government of Spain and the
project EU‑LIFE 18 IPC/ES/000001 (Urban Klima 2050). Maíra Aguiar and Aitor
Cevidanes acknowledges the financial support by the Ministerio de Ciencia e
Innovacion (MICINN) of the Spanish Government and European Union Next
Generation EU/PRTR through the Ramon y Cajal grants RYC2021‑031380‑I and
RYC2021‑033084‑I, respectively
Safety and Efficacy in the Transcortical and Transsylvian Approach in Insular High-Grade Gliomas: A Comparative Series of 58 Patients
No full textGliomas within the insular region represent one of the most challenging problems in neurosurgical oncology. There are two main surgical approaches to address the complex vascular network and functional areas around the insula: the transsylvian approach and the transcortical approach. In the literature, there is not a clear consensus on the best approach in terms of safety and efficacy. The purpose of this study is to evaluate the effectiveness of these approaches and to analyze prognostic factors on the natural history of insular gliomas. Patients with newly diagnosed high-grade insular gliomas who underwent surgery between January 2019 and June 2024 were analyzed. The series was analyzed according to the classification of Berger–Sanai and Yaşargil. The Karnofsky performance score (KPS), extent of resection (EOR), progression-free survival (PFS), and overall survival (OS) were considered the outcome measures. A total of 58 primary high-grade insular glioma patients were enrolled in this study. The IDH mutation was found in 13/58 (22.4%); specifically, 3/13 (23.1%) were grade 4, and 10/13 (76.9%) were grade 3. Furthermore, 40/58 patients (69%) underwent gross total resection (GTR), 15 patients (26%) subtotal resection, and 3 patients (5%) partial resection. Middle cerebral artery encasement negatively affected the OS. GTR, radiotherapy, KPS, and autonomous deambulation at a month after surgery positively affected the OS. The surgical approach used was transsylvian and transcortical in 11 and 47 cases, respectively. The comparison between the two different approaches did not display differences in terms of neurological deficits and OS (p > 0.05). The transcortical approach was related to the greater achievement of GTR (p = 0.031). According to the Berger–Sanai classification, the transcortical approach has higher EOR and postoperative KPS when the lesion is in zone III-IV (p = 0.029). Greater resection of insular gliomas can be achieved with an acceptable morbidity profile and is predictive of improved OS. Both the transsylvian and transcortical corridors to the insula are associated with low morbidity profiles. The transcortical approach with intraoperative mapping is more favorable for achieving greater EOR, particularly in gliomas within the inferior border of the Sylvian fissure