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Background and Objectives: There is an increasing and renewed interest in Electronic Health Records (EHRs) as a substantial information source for clinical decision making. Consequently, automatic de-identification of EHRs is an indispensable task, since their dissociation from personal data is a necessary prerequisite for their dissemination. Nevertheless, the bulk of prior research in this domain has been conducted using English EHRs, given the limited availability of annotated corpora in other languages, including Spanish., Methods: In this study, the automatic de-identification of medical documents in Spanish was explored. A private corpus comprising 599 genuine clinical cases was annotated with eight different categories of protected health information. The prediction problem was approached as a named entity recognition task and two deep learning-based methodologies were developed. The first strategy was based on recurrent neural networks (RNN) and the second, an end-to-end approach, was based on Transformers. In addition, we have implemented a procedure to expand the amount of texts employed for model training., Results: Our findings demonstrate that Transformers surpass RNNs in the de-identification of clinical data in Spanish. Particularly noteworthy is the excellent performance of the XLM-RoBERTa large Transformer, achieving a rigorous strict-match micro-average of 0.946 for precision, 0.954 for recall, and an F1 score of 0.95 when applied to the amplified version of the corpus. Furthermore, a web-based application has been created to assist specialized clinicians in de-identifying EHRs through the aid of the implemented models., Conclusion: The study's conclusions showcase the practical applicability of the state-of-the-art Transformers models for precise de-identification of clinical notes in real-world medical settings in Spanish, with the potential to improve performance if continual pre-training strategies are implemented., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Despite the increasing investments in Latin American healthcare, the corresponding improvement in population health is not proportional. This discrepancy may be attributed to the efficiency of resource utilization. This study used the data envelopment analysis (DEA) methodology to assess the efficiency of healthcare systems in 23 Latin American and Caribbean countries., Methods: We used the most recent data from the World Bank (2017). Our analysis included healthcare expenditure (percentage of gross domestic product), hospital beds per 1000 inhabitants, physicians per 1000 inhabitants, and nurses per 1000 inhabitants as input variables. Life expectancy at birth and infant mortality rate were used as output variables. We conducted sensitivity analyses on model parameters to understand their influence. Linear and Tobit regressions were developed to comprehend the relationship between variables and DEA scores. Finally, we performed a temporal analysis of efficiencies to identify trends and patterns within the sample., Results: The DEA model classified 13 of the 23 analyzed countries as efficient, with 10 consistently maintaining this level throughout the 10 years of the temporal analysis: Belize, Chile, Colombia, Costa Rica, Cuba, Guatemala, Honduras, Jamaica, Mexico, and Peru. Conversely, 4 countries were never considered efficient: Barbados, Brazil, Nicaragua, and Uruguay., Conclusions: This study provides valuable insights for Latin American and Caribbean countries, offering a roadmap to manage their resources better and provide quality and equitable service to their people despite economic challenges., Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section., (Copyright © 2024 International Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Background and Purpose: Adopting telemedicine (TM) enables improved access to specialized care and reduces barriers. The aim was to assess the cost-utility of a coadjutant multidisciplinary TM programme for fall prevention compared to standard in-office visits for individuals with Parkinson's disease (PD)., Methods: This was an 8-month single-blind randomized controlled trial. TM and control groups received in-office visits and standard management care at baseline, 4 and 8 months. In addition, the TM group received remote multidisciplinary visits for 4 months. Gait, motor and non-motor symptoms, daily living activities, balance and frailty were measured using PD-recommended rating scales and wearable sensors. Clinical characteristics were compared at each visit using baseline scores, gender and age as covariates. The incremental cost-effectiveness ratio (ICER) and quality-adjusted life years (QALYs) were calculated at each visit., Results: Fifty patients were included: 25 patients in the TM group (48% males, mean age 71.1 ± 9.0 years) and 25 patients in the control group (52% males, mean age 69.2 ± 9.4 years). Compared to controls, in the TM group similar QALYs were found but, in contrast, significant improvements in daily living activities, depression, apathy, freezing of gait, balance, quality of life and frailty (all p values <0.05). The use of coadjutant TM intervention in addition to in-office visits was efficient for depression, apathy, freezing of gait, balance and frailty with ICERs, ranging from 91.55 € for non-motor symptoms to 1677.4 € for frailty., Conclusions: Telemedicine could be considered an efficient coadjutant intervention for PD, especially for non-motor symptoms, enhancing health outcomes and accessibility., (© 2025 The Author(s). European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Commercially pure titanium (c.p. Ti) and Ti6Al4V alloys are the most widely used metallic biomaterials in the biomedical sector. However, their high rigidity and the controversial toxicity of their alloying elements often compromise their clinical success. The use of porous β-Titanium alloys is proposed as a solution to these issues. In this regard, it is necessary to implement economic, repetitive, and non-destructive measurement techniques that allow for the semi-quantitative evaluation of the chemical nature of the implant, its microstructural characteristics, and/or surface changes. This study proposes the use of simple measurement protocols based on electrical impedance measurements, correlating them with the porosity inherent to processing conditions (pressure and temperature), as well as the chemical composition of the implant. Results revealed a clear direct relationship between porosity and electrical impedance. The percentage and/or size of the porosity decrease with an increase in compaction pressure and temperature. Moreover, there is a notable influence of the frequency used in the measurements obtained. Additionally, the sensitivity of this measurement technique has enabled the evaluation of differences in chemical composition and the detection of intermetallics in the implants. For the first time in the literature, this research establishes relationships between stiffness and electrical impedance, using approximations and models for the observed trends. All the results obtained corroborate the appropriateness of the technique to achieve the real-time characterization of Titanium implants, in an efficient and non-invasive way., (© 2024 The Author(s). Journal of Biomedical Materials Research Part A published by Wiley Periodicals LLC.)

Chimp optimization algorithm (CHOA) is a recently developed nature-inspired technique that mimics the swarm intelligence of chimpanzee colonies. However, the original CHOA suffers from slow convergence and a tendency to reach local optima when dealing with multidimensional problems. To address these limitations, we propose TASR-CHOA, a twofold adaptive stochastic reinforced variant. The TASR-CHOA algorithm integrates two novel methodologies: a stochastic approach to improve the speed at which convergence is achieved and a dual adaptive weighting approach to optimize the exploration of early patterns, which refer to initial trends or behaviors in the algorithm's convergence process during the early stages of iterations and the exploitation of subsequent tendencies, indicating how these initial trends develop over time as the algorithm iterates and refines its search. To evaluate TASR-CHOA, we apply it to 29 conventional optimization benchmark functions, 10 IEEE CEC-06 benchmarks, 30 complicated IEEE CEC-BC benchmark functions, and ten well-known benchmark real-world challenges. We evaluate TASR-CHOA against 4 categorical optimization techniques as well as 18 top IEEE CEC-BC algorithms. Based on our broad investigation done using three statistical tests, we claim that TASR-CHOA outperforms the majority of the algorithms since within a position takes the best place, 54 out of 73 evaluation functions and engineering problems. In other cases, the results are almost the same as those of SHADE and CMA-ES over several comparisons. As an illustrative application of this joint approach, a computer-aided fire detection task is performed using a deep convolutional neural network combined with TASR-CHOA. We also outline the algorithm executed in steps, indicating computational complexity, which is O(NI×NV) + O(NI×NV×6) + O(NI×NV + 2×NI×NV) as a function of number of individuals (NI) and dimensions (NV)., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. The authors declare that there is no conflict of interest., (© 2024. The Author(s).)

From the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term "cognon". A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the 'what', 'where' and 'when'., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© The Author(s) 2023.)

Background: The increase in population longevity often occurs in contexts of inequity and relative poverty, accompanied by economic deterioration. This becomes a social determinant that has a direct impact on food security. This phenomenon particularly affects certain groups and territories, although there is still a lack of disaggregated references. Intersections between factors such as being a rural inhabitant, Indigenous, woman, or elderly person are observed in relation to food security, which forces us to pay greater attention to gaps that have remained invisible for years., Objective: The objective of this study is to analyze the main factors that affect the food security of Mapuche men and women over 60 years of age living in the rural area of Temuco, Chile., Method: Qualitative, descriptive, and interpretative research was carried out, observing the process from the interpretative symbolic paradigm and the complexity approach., Results: The data are made up of the discourses of these subjects, whose analysis allowed for the identification of results. These results show that producing their own food enables rural Mapuche elders to achieve food security. The cultural food heritage preserved by Mapuche elders, especially women, acts as a facilitating factor, as do community spaces that reinforce their culture. Among the obstacles to food security are migration to the city for work, pathological aging, and the limited production of culturally healthy foods (affected by environmental problems, cultural changes, the destabilization of group identity, and public policies that are incongruent with the territorial worldview)., Conclusions: While rural Mapuche elders retain valuable practices for their food security, inadequate policies, migration and environmental degradation present significant challenges.

The production of phonation involves very complex processes, linked to the physical, clinical, and emotional state of the speaker. Thus, in populations with neurological diseases, it is possible to find the imprint in the voice signal left by the deterioration of certain cortical areas or part of the neurocognitive mechanisms that are involved in speech. In previous works, the authors determined the relationship between the pathological characteristics of the voice of the speakers with Smith-Magenis syndrome (SMS) and a lower value in the cepstral peak prominence (CPP) with respect to normative speakers. They also described the presence of subharmonics in their voices., Objectives: The present study aims to verify whether both characteristics can be used simultaneously to differentiate SMS voices from neurotypical voices. It will also be analyzed if there is variation in the trajectory of the formants coinciding with the subharmonics., Methods: To do this, the effect of subharmonics in the voices of 12 SMS individuals was isolated to see if they were responsible for the lower CPP values. An evaluation of the CPP was also carried out in the areas of subharmonic presence, from the peak that reflected the value of f0, rather than using the most prominent peak. This offered us a baseline for the CPP value in the presence of subharmonics. It was checked if changes in the formants occurred synchronously to the appearance of those subharmonics. If so, the muscles that control the position of the jaw and tongue would be affected at the same time as the larynx. The latter was difficult to observe since the samples were very short. A comparison of phonatory performance of a sustained /a/ between a normotypical group and non-normotypical group of children was carried out. These groups were balanced and matched in age and gender. The Spanish Association of Smith-Magenis Syndrome (ASME) provides almost 20% of the population in Spain., Results: The CPP allows differentiating between normative speakers and those with SMS, even when isolating the effect of subharmonics., Conclusions: The CPP is a robust index for determining the degree of dysphonia. It makes it possible to differentiate pathological voices from healthy voices even when subharmonics are present. The presence of subharmonics is a characteristic of voices of SMS individuals and is not present in healthy ones. Both indexes can be used simultaneously to differentiate SMS voices from neurotypical voices., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The proportion of older people will soon include nearly a quarter of the world population. This leads to an increased prevalence of non-communicable diseases such as Alzheimer's disease (AD), a progressive neurodegenerative disorder and the most common dementia. mild cognitive impairment (MCI) can be considered its prodromal stage. The early diagnosis of AD is a huge issue. We face it by solving these classification tasks: MCI-AD and cognitively normal (CN)-MCI-AD., Methods: An intelligent computing system has been developed and implemented to face both challenges. A non-neural preprocessing module was followed by a processing one based on a hybrid and ontogenetic neural architecture, the modular hybrid growing neural gas (MyGNG). The MyGNG is hierarchically organized, with a growing neural gas (GNG) for clustering followed by a perceptron for labeling. For each task, 495 and 819 patients from the Alzheimer's disease neuroimaging initiative (ADNI) database were used, respectively, each with 211 characteristics., Results: Encouraging results have been obtained in the MCI-AD classification task, reaching values of area under the curve (AUC) of 0.96 and sensitivity of 0.91, whereas 0.86 and 0.9 in CN-MCI-AD. Furthermore, a comparative study with popular machine learning (ML) models was also performed for each of these tasks., Conclusions: The MyGNG proved to be a better computational solution than the other ML methods analyzed. Also, it had a similar performance to other deep learning schemes with neuroimaging. Our findings suggest that our proposal may be an interesting computing solution for the early diagnosis of AD., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Data used in this work were obtained from the ADNI repository, which imposes some restrictions on public data access and sharing., (© The Author(s) 2024.)

The main objective of this study is to evaluate the low-cost, open-source HEGduino device as a tissue oximetry monitor to advance the research of somatic NIRS monitoring. Specifically, this study analyzes the use of this portable functional NIRS system for detecting the cessation of blood flow due to vascular occlusion in an upper limb. 19 healthy patients aged between 25 and 50 were recruited and monitored using HEGduino device. Participants underwent a vascular occlusion test on one forearm. Raw values collected by HEGduino as well as the processed variables derived from the measurements were registered. Additional variables to characterize the signal noise during the tests were also recorded. The results of the data distribution curves for all the subjects in the study accurately detected the physiological events associated with transient tissue ischemia. The statistical analysis of the recorded data showed that the difference between the baseline values recorded by the red led (RED) and its normalized minimum variable was always different from zero (p < 0.014). Furthermore, the difference between the normalized baseline values recorded by the infrared led (IR) and the corresponding normalized minimum value was also different from zero (p < 0.001). The R-squared coefficient of determination for the noise variables considered in this study on the normalized RED and IR values was 0.08 and 0.105, respectively. The study confirms the potential of HEGduino system to detect an interruption of the blood flow by means of variations in regional tissue oxygen saturation. This study demonstrates the potential of the HEGduino device as a monitoring alternative to advance the study of the applicability of NIRS in muscle tissue oximetry., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work needs to be done in this regard. Among all brain regions, the hippocampus stands out as an autoassociative short-term memory with the capacity to learn and recall memories from any fragment of them. These characteristics make the hippocampus an ideal candidate for developing bio-inspired learning systems that, in addition, resemble content-addressable memories. Therefore, in this work we propose a bio-inspired spiking content-addressable memory model based on the CA3 region of the hippocampus with the ability to learn, forget and recall memories, both orthogonal and non-orthogonal, from any fragment of them. The model was implemented on the SpiNNaker hardware platform using Spiking Neural Networks. A set of experiments based on functional, stress and applicability tests were performed to demonstrate its correct functioning. This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

An energy-based theory of autoresonance in driven dissipative chains of coupled generic oscillators is discussed on the basis of a variational principle concerning the energy functional. The theory is applied to chains of delayed Duffing-Ueda oscillators and the equations that together govern the autoresonance forces and solutions are derived and solved analytically for generic values of parameters and initial conditions, including the case of quenched time-delay disorder. Remarkably, the presence of retarded potentials with time-delayed feedback drastically modify the autoresonance scenario preventing the growth of the energy oscillation over specific regions of the parameter space. Additionally, effective harmonic forces with a slowly varying frequency are derived from the exact autoresonant excitations and the effectiveness of the theory is demonstrated at suppressing the chaos induced by homogeneous periodic excitations in such oscillator chains. Numerical experiments confirmed all the theoretical predictions.

Emotion recognition through speech is a technique employed in various scenarios of Human-Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%.

Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission of the virus. Minimizing missed opportunities for HIV diagnosis when a patient visits a healthcare facility is essential in restraining the epidemic and working toward its eventual elimination. Most state-of-the-art proposals employ machine learning (ML) methods and structured data to enhance HIV diagnoses, however, there is a dearth of recent proposals utilizing unstructured textual data from Electronic Health Records (EHRs). In this work, we propose to use only the unstructured text of the clinical notes as evidence for the classification of patients as suspected or not suspected. For this purpose, we first compile a dataset of real clinical notes from a hospital with patients classified as suspects and non-suspects of having HIV. Then, we evaluate the effectiveness of two types of classification models to identify patients suspected of being infected with the virus: classical ML algorithms and two Large Language Models (LLMs) from the biomedical domain in Spanish. The results show that both LLMs outperform classical ML algorithms in the two settings we explore: one dataset version is balanced, containing an equal number of suspicious and non-suspicious patients, while the other reflects the real distribution of patients in the hospital, being unbalanced. We obtain F 1 score figures of 94.7 with both LLMs in the unbalanced setting, while in the balance one, RoBERTa Bio model outperforms the other one with a F 1 score of 95.7. The findings indicate that leveraging unstructured text with LLMs in the biomedical domain yields promising outcomes in diminishing missed opportunities for HIV diagnosis. A tool based on our system could assist a doctor in deciding whether a patient in consultation should undergo a serological test., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: The design and development of patient-centered digital health solutions requires user involvement, for example through usability testing. Although there are guidelines for conducting usability tests, there is a lack of knowledge about the technical, human, and organizational factors that influence the success of the tests., Objective: To summarize the success factors of usability testing in the context of patient-centered digital health solutions., Method: We considered three case studies and collected experiences related to time management, relevance of results and challenges encountered., Results: Success factors relate to participant privacy and data protection, test environment setup, device and application readiness, user comfort and accessibility, test tools and procedures, and adaptability to user limitations., Conclusions: Small organizational and technical details can have a big impact on the outcome of a usability test. Considering the aspects mentioned in this paper will not only save resources but also the trust of the participating patients.

Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics.

Background: Executive function (EF) deficits are a significant risk factor for falls among older adults (OAs). However, relationship between EF subdomains (shifting, updating, and inhibition), postural balance (PB), and fall risk in healthy OAs, remains poorly understood., Objective: This study aimed to investigate the relationship between EF subdomains (shifting, updating, and inhibition) and PB, and to assess their impact on risk of falls in community-dwelling OAs., Methods: A cross-sectional study involving 50 OAs aged over 60 years (average age of 72 years) was conducted. Participants underwent assessments of EF subdomains and PB using validated tests. A correlation analysis was employed to examine the relationships between EF and PB., Results: The study revealed significant correlations between subdomains and PB. Mental set shifting (r = -.539; p < .001) and inhibition (r = -.395; p = .050) exhibited inverse relationships with PB. Stepwise multiple linear regression showed that Trail Making Test Part B was associated with the PB (R2 = .42, p < .001)., Conclusion: These findings highlight the importance of assessing EF subdomains, particularly shifting and inhibition, to identify risk of falls. Trail Making Test Part B largely explains the variability of the PB. Integrating PB assessments and EF training, such as the Mini-BESTest, into routine care can be vital for fall prevention strategies. Significance/Implications: This knowledge underscores the need for cognitive training interventions focusing on shifting and inhibition to enhance PB and potentially reduce falls. Additionally, incorporation of EF assessment tools as Trail Making Test Part B and the Mini-BESTest into routine clinical practice for community-dwelling OAs is recommended to address fall prevention strategies.

When inferring the evolution of a gene/gene family, it is advisable to use all available coding sequences (CDS) from as many species genomes as possible in order to infer and date all gene duplications and losses. Nowadays, this means using hundreds or even thousands of CDSs, which makes the inferred phylogenetic trees difficult to visualize and interpret. Therefore, it is useful to have an automated way of collapsing large phylogenetic trees according to a taxonomic term decided by the user (family, class, or order, for instance), in order to highlight the minimal set of sequences that should be used to recapitulate the full history of the gene/gene family being studied at that taxonomic level, that can be refined using additional software. Here we present the Phylogenetic Tree Collapser (PTC) program (https://github.com/pegi3s/phylogenetic-tree-collapser), a flexible tool for automated tree collapsing using taxonomic information, that can be easily used by researchers without a background in informatics, since it only requires the installation of Docker, Podman or Singularity. The utility of PTC is demonstrated by addressing the evolution of the ascorbic acid synthesis pathway in insects. A Docker image is available at Docker Hub (https://hub.docker.com/r/pegi3s/phylogenetic-tree-collapser) with PTC installed and ready-to-run., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

The SunoCaps dataset aims to provide an innovative contribution to music data. Expert description of human-made musical pieces, from the widely used MusicCaps dataset, are used as prompts for generating complete songs for this dataset. This Automatic Music Generation is done with the state-of-the-art Suno generator of audio-based music. A subset of 64 pieces from MusicCaps is currently included, with a total of 256 generated entries. This total stems from generating four different variations for each human piece; two versions based on the original caption and two versions based on the original aspect description. As an AI-generated music dataset, SunoCaps also includes expert-based information on prompt alignment, with the main differences between prompt and final generation annotated. Furthermore, annotations describing the main discrete emotions induced by the piece. This dataset can have an array of implementations, such as creating and improving music generation validation tools, training systems for multi-layered architectures and the optimization of music emotion estimation systems., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Asthma is a chronic non-communicable disease considered a serious health problem worldwide. The Chronic Care Model promotes the self-management of patients' chronic conditions. This process requires an active engagement of health professionals to provide education for patients to manage their disease better., Aim: this study sought to explore the education process carried out by primary health care providers for asthma patients in two regions of Chile., Methods: Nine group interviews with 35 health providers in nine primary health care centers were conducted in two of the country's major urban areas, the Valparaíso and Biobío Regions. Data were analyzed using an inductive approach, with coding followed by category construction and interpretation., Results: Based mainly on age and educational level, health professionals construct a patient profile to adjust the information provided and the strategies employed to educate patients. The education is focused on pharmacological treatment and inhalation techniques. Educational strategies are primarily direct instruction through individual one-on-one sessions. Fewer actions were described to help patients choose, control, and correct their daily routines and lifestyle and involve patients in self-awareness and self-regulating their behavior., Conclusions: The health care education process for asthma patients focuses on delivering information about the disease and medical treatment. Some issues of the Chronic Care Model, which promotes self-management, are still not addressed.

Voltage-gated Ca V 2.1 (P/Q-type) Ca 2+ channels play a crucial role in regulating neurotransmitter release, thus contributing to synaptic plasticity and to processes such as learning and memory. Despite their recognized importance in neural function, there is limited information on their potential involvement in neurodegenerative conditions such as Alzheimer's disease (AD). Here, we aimed to explore the impact of AD pathology on the density and nanoscale compartmentalization of Ca V 2.1 channels in the hippocampus in association with GABA B receptors. Histoblotting experiments showed that the density of Ca V 2.1 channel was significantly reduced in the hippocampus of APP/PS1 mice in a laminar-dependent manner. Ca V 2.1 channel was enriched in the active zone of the axon terminals and was present at a very low density over the surface of dendritic tree of the CA1 pyramidal cells, as shown by quantitative SDS-digested freeze-fracture replica labelling (SDS-FRL). In APP/PS1 mice, the density of Ca V 2.1 channel in the active zone was significantly reduced in the strata radiatum and lacunosum-moleculare, while it remained unaltered in the stratum oriens. The decline in Cav2.1 channel density was found to be associated with a corresponding impairment in the GABAergic synaptic function, as evidenced by electrophysiological experiments carried out in the hippocampus of APP/PS1 mice. Remarkably, double SDS-FRL showed a co-clustering of Ca V 2.1 channel and GABA B1 receptor in nanodomains (~40-50 nm) in wild type mice, while in APP/PS1 mice this nanoarchitecture was absent. Together, these findings suggest that the AD pathology-induced reduction in Ca V 2.1 channel density and Ca V 2.1-GABA B1 de-clustering may play a role in the synaptic transmission alterations shown in the AD hippocampus. Therefore, uncovering these layer-dependent changes in P/Q calcium currents associated with AD pathology can benefit the development of future strategies for AD management., (© 2024 The Author(s). Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.)

Sex differences have been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to sex and the conditions that generate these effects. The present work studied the effects of a drill-and-practice mathematical game on primary students. The study focused on an analysis by sex, measuring motivation and learning in the practice activity. Also, two instructional mechanics were considered regarding the question answering to search for possible differences: a multiple-try feedback (MTF) condition and a single-try feedback (STF) condition. A total of 81 students from four courses and two schools participated in the intervention. The study's main findings were as follows: (a) the girls outperformed the boys in terms of the students' learning gains; (b) the girls presented lower levels of competence and autonomy than the boys; (c) under MTF, the girls presented lower levels of autonomy but no differences in competence contrasted with the boys; (d) under STF, the girls presented lower levels of competence but no differences in autonomy contrasted with the boys; (e) no sex differences existed in interest, effort, and value, in general, as per the instructional condition. This study enhances the knowledge of sex differences under diverse instructional settings, in particular providing insights into the possible differences by sex when varying the number of attempts provided to students.

Heart failure is a severe medical condition with an important worldwide incidence that occurs when the heart is unable to efficiently pump the patient's blood throughout the body. The monitoring of edema in the lower limbs is one of the most efficient ways to control the evolution of the condition. Impedance spectroscopy has been proposed as an efficient technique to monitor body volume in patients with heart failure. It is necessary to research new wearable devices for remote patient monitoring, which can be easily worn by patients in a continuous way. In this work, we design and implement new wearable textile electrodes for the monitoring of edema evolution in patients with heart failure. Impedance spectroscopy measurements were carried out in 5 healthy controls and 2 patients with heart failure using our wearable electrodes for 3 days. The results show the appropriateness of impedance spectroscopy and our wearable electrodes to monitor body volume evolution. Impedance spectroscopy is shown to be an efficient marker of the presence of edema in heart failure patients. Initial patient positive feedback was obtained for the use of the wearable device.

Background: The initial version of SEDA assists life science researchers without programming skills with the preparation of DNA and protein sequence FASTA files for multiple bioinformatics applications. However, the initial version of SEDA lacks a command-line interface for more advanced users and does not allow the creation of automated analysis pipelines., Results: The present paper discusses the updates of the new SEDA release, including the addition of a complete command-line interface, new functionalities like gene annotation, a framework for automated pipelines, and improved integration in Linux environments., Conclusion: SEDA is an open-source Java application and can be installed using the different distributions available ( https://www.sing-group.org/seda/download.html ) as well as through a Docker image ( https://hub.docker.com/r/pegi3s/seda ). It is released under a GPL-3.0 license, and its source code is publicly accessible on GitHub ( https://github.com/sing-group/seda ). The software version at the time of submission is archived at Zenodo (version v1.6.0, http://doi.org/10.5281/zenodo.10201605 )., (© 2024. The Author(s).)

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

Background: The protocol described in this paper is part of a research project coordinated between three Spanish universities, where a technology aimed at improving the quality of life of people with cerebral palsy will be developed. Part of the proposed technology will consist of an interface and a series of applications to increase motivation for daily physical activity. The basis of these developments is the measurement of the emotional state of the subjects., Methods: The experimental protocol is designed with two research objectives, on the one hand to identify the emotional state through physiological signals, and on the other to determine whether music can be a motivating factor to promote physical activity. It is specifically designed for subjects with cerebral palsy, taking into account the special characteristics of this population. These are people with whom it is difficult to use questionnaires to have a basis to contrast with the measured physiological signals, so measurements must be taken in carefully chosen daily-life situations., Discussion: We hope our findings show which physiological parameters are the most robust to measure the emotional state and how to design rehabilitation and physical activity promotion routines that are motivating, in addition to being able to avoid risk factors during the performance of these routines., Trial Registration: NCT05621057.

Background: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications., Objective: The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care., Methods: We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items., Results: The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data., Conclusions: Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice., (©Kerstin Denecke, Richard May, LLMHealthGroup, Octavio Rivera Romero. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.05.2024.)

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional integer programming solutions may become inefficient in real-life instances. Given this complexity, various metaheuristics have been successfully applied to solve the set-covering problem and related issues. This study introduces, implements, and analyzes a novel metaheuristic inspired by the well-established Growth Optimizer algorithm. Drawing insights from human behavioral patterns, this approach has shown promise in optimizing complex problems in continuous domains, where experimental results demonstrate the effectiveness and competitiveness of the metaheuristic compared to other strategies. The Growth Optimizer algorithm is modified and adapted to the realm of binary optimization for solving the set-covering problem, resulting in the creation of the Binary Growth Optimizer algorithm. Upon the implementation and analysis of its outcomes, the findings illustrate its capability to achieve competitive and efficient solutions in terms of resolution time and result quality.

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well with respect to a target algorithm belonging to a predefined portfolio but are also diverse with respect to their features. Our approach uses a novelty search algorithm with a linearly weighted fitness function that balances novelty and performance to generate a large set of diverse and discriminatory instances in a single run of the algorithm. We consider two definitions of novelty: (1) with respect to discriminatory performance within a portfolio of solvers; (2) with respect to the features of the evolved instances. We evaluate the proposed method with respect to its ability to generate diverse and discriminatory instances in two domains (knapsack and bin-packing), comparing to another well-known quality diversity method, Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and an evolutionary algorithm that only evolves for discriminatory behaviour. The results demonstrate that the novelty search method outperforms its competitors in terms of coverage of the space and its ability to generate instances that are diverse regarding the relative size of the "performance gap" between the target solver and the remaining solvers in the portfolio. Moreover, for the Knapsack domain, we also show that we are able to generate novel instances in regions of an instance space not covered by existing benchmarks using a portfolio of state-of-the-art solvers. Finally, we demonstrate that the method is robust to different portfolios of solvers (stochastic approaches, deterministic heuristics and state-of-the-art methods), thereby providing further evidence of its generality., (© 2024 Massachusetts Institute of Technology.)

Background: The relationship between dynamic cerebral autoregulation (dCA) and functional outcome after acute ischemic stroke (AIS) is unclear. Previous studies are limited by small sample sizes and heterogeneity., Methods: We performed a 1-stage individual patient data meta-analysis to investigate associations between dCA and functional outcome after AIS. Participating centers were identified through a systematic search of the literature and direct invitation. We included centers with dCA data within 1 year of AIS in adults aged over 18 years, excluding intracerebral or subarachnoid hemorrhage. Data were obtained on phase, gain, coherence, and autoregulation index derived from transfer function analysis at low-frequency and very low-frequency bands. Cerebral blood velocity, arterial pressure, end-tidal carbon dioxide, heart rate, stroke severity and sub-type, and comorbidities were collected where available. Data were grouped into 4 time points after AIS: <24 hours, 24 to 72 hours, 4 to 7 days, and >3 months. The modified Rankin Scale assessed functional outcome at 3 months. Modified Rankin Scale was analyzed as both dichotomized (0 to 2 versus 3 to 6) and ordinal (modified Rankin Scale scores, 0-6) outcomes. Univariable and multivariable analyses were conducted to identify significant relationships between dCA parameters, comorbidities, and outcomes, for each time point using generalized linear (dichotomized outcome), or cumulative link (ordinal outcome) mixed models. The participating center was modeled as a random intercept to generate odds ratios with 95% CIs., Results: The sample included 384 individuals (35% women) from 7 centers, aged 66.3±13.7 years, with predominantly nonlacunar stroke (n=348, 69%). In the affected hemisphere, higher phase at very low-frequency predicted better outcome (dichotomized modified Rankin Scale) at <24 (crude odds ratios, 2.17 [95% CI, 1.47-3.19]; P <0.001) hours, 24-72 (crude odds ratios, 1.95 [95% CI, 1.21-3.13]; P =0.006) hours, and phase at low-frequency predicted outcome at 3 (crude odds ratios, 3.03 [95% CI, 1.10-8.33]; P =0.032) months. These results remained after covariate adjustment., Conclusions: Greater transfer function analysis-derived phase was associated with improved functional outcome at 3 months after AIS. dCA parameters in the early phase of AIS may help to predict functional outcome., Competing Interests: Disclosures None.

In the teaching of the operating systems course, which is part of computer engineering degrees, a thorough understanding of processor scheduling algorithms is crucial. However, it has been identified that the current knowledge of classical algorithms is insufficient in the present context. Therefore, it is proposed to conduct a review of the state of the art in the field to identify new trends and algorithms that can enhance the teaching of the subject and improve student training. As a result, the state of the art is thoroughly reviewed, and study sheets are designed to facilitate the comprehension of the algorithms. Additionally, a software simulator is developed to compare different algorithms in a controlled environment, allowing for the validation of the most promising ones for classroom teaching., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

This study primarily aimed to explore the capabilities of digitalisation in the healthcare context, focusing on a specific disease. In this case, the study examined the potential of remote monitoring of gait to address the sensitivity of multiple sclerosis progression to gait characteristics by adopting a non-invasive approach to remotely quantify gait disturbances in a patient's daily life. To better understand the managerial aspects associated with this approach, the researchers conducted a literature review along with a set of semi-structured interviews. The target population included MS patients as well as the key agents involved in their care: patients' family members, neurologists, MS nurses, physiotherapists, medical directors, and pharmacist. The study identifies the perceived barriers and drivers that could contribute to the successful deployment of PSS remote gait monitoring as a healthcare service: i) At mega-level governance. Implications on privacy and security data are notable barriers missing on the speech. ii) At macro level, funding is highlighted as main barrier. The cost and lack of health system subsidies may render initiatives unsustainable, as emphasised by the interviewees. iii) At meso level, useable data is recognised as a driver. The data collection process can align with diverse interests to create value and business opportunities for the ecosystem actors, enhance care, attract stakeholders, such as insurers and pharma, and form partnerships. iv) At micro-level processes, we find two potential barriers: wearable device and app usability (comfort, navigation, efficiency) and organisational/behavioural aspects (training, digital affinity, skills), which are crucial for value creation in innovation ecosystems among patients and healthcare professionals. Finally, we find an interesting gap in the literature and interviews. Stakeholders' limited awareness of technological demands, especially from information technologies, for a successful long-term service, can be consider two key barriers for PSS., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Objectives: Secondary infections due to transmission via the duodenoscope have been reported in up to 3% of endoscopic retrograde cholangiopancreatographies. The use of single-use duodenoscopes has been suggested. We investigate the cost-effectiveness of these duodenoscopes use in cholangiopancreatography., Materials and Methods: A cost-effectiveness analysis was implemented to compare the performance of cholangiopancreatographies with reusable duodenoscopes versus single-use duodenoscopes. Effectiveness was analyzed by calculating quality-adjusted life years (QALY) from the perspective of the National Health System. Possibility of crossover from single-use to reusable duodenoscopes was considered. A willingness-to-pay of €25,000/QALY was set, the incremental cost-effectiveness ratio (ICER) was calculated, and deterministic and probabilistic sensitivity analyses were performed., Results: Considering cholangiopancreatographies with single-use and reusable duodenoscopes at a cost of €2900 and €1333, respectively, and a 10% rate of single-use duodenoscopes, ICER was greater than €3,000,000/QALY. A lower single-use duodenoscope cost of €1211 resulted in an ICER of €23,583/QALY. When the unit cost of the single-use duodenoscope was €1211, a crossover rate of more than 9.5% made the use of the single-use duodenoscope inefficient., Conclusions: Single-use duodenoscopes are cost-effective in a proportion of cholangiopancreatographies if its cost is reduced. Increased crossover rate makes single-use duodenoscope use not cost-effective., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Significance: Near-infrared laser illumination is a non-invasive alternative/complement to classical stimulation methods in neuroscience but the mechanisms underlying its action on neuronal dynamics remain unclear. Most studies deal with high-frequency pulsed protocols and stationary characterizations disregarding the dynamic modulatory effect of sustained and activity-dependent stimulation. The understanding of such modulation and its widespread dissemination can help to develop specific interventions for research applications and treatments for neural disorders., Aim: We quantified the effect of continuous-wave near-infrared (CW-NIR) laser illumination on single neuron dynamics using sustained stimulation and an open-source activity-dependent protocol to identify the biophysical mechanisms underlying this modulation and its time course., Approach: We characterized the effect by simultaneously performing long intracellular recordings of membrane potential while delivering sustained and closed-loop CW-NIR laser stimulation. We used waveform metrics and conductance-based models to assess the role of specific biophysical candidates on the modulation., Results: We show that CW-NIR sustained illumination asymmetrically accelerates action potential dynamics and the spiking rate on single neurons, while closed-loop stimulation unveils its action at different phases of the neuron dynamics. Our model study points out the action of CW-NIR on specific ionic-channels and the key role of temperature on channel properties to explain the modulatory effect., Conclusions: Both sustained and activity-dependent CW-NIR stimulation effectively modulate neuronal dynamics by a combination of biophysical mechanisms. Our open-source protocols can help to disseminate this non-invasive optical stimulation in novel research and clinical applications., (© 2024 The Authors.)

Skin blemishes can be caused by multiple events or diseases and, in some cases, it is difficult to distinguish where they come from. Therefore, there may be cases with a dangerous origin that go unnoticed or the opposite case (which can lead to overcrowding of health services). To avoid this, the use of artificial intelligence-based classifiers using images taken with mobile devices is proposed; this would help in the initial screening process and provide some information to the patient prior to their final diagnosis. To this end, this work proposes an optimization mechanism based on two phases in which a global search for the best classifiers (from among more than 150 combinations) is carried out, and, in the second phase, the best candidates are subjected to a phase of evaluation of the robustness of the system by applying the cross-validation technique. The results obtained reach 99.95% accuracy for the best case and 99.75% AUC. Comparing the developed classifier with previous works, an improvement in terms of classification rate is appreciated, as well as in the reduction of the classifier complexity, which allows our classifier to be integrated in a specific purpose system with few computational resources., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The vast amount of genome sequence data that is available, and that is predicted to drastically increase in the near future, can only be efficiently dealt with by building automated pipelines. Indeed, the Earth Biogenome Project will produce high-quality reference genome sequences for all 1.8 million named living eukaryote species, providing unprecedented insight into the evolution of genes and gene families, and thus on biological issues. Here, new modules for gene annotation, further BLAST search algorithms, further multiple sequence alignment methods, the adding of reference sequences, further tree rooting methods, the estimation of rates of synonymous and nonsynonymous substitutions, and the identification of positively selected amino acid sites, have been added to auto-phylo (version 2), a recently developed software to address biological problems using phylogenetic inferences. Additionally, we present auto-phylo-pipeliner, a graphical user interface application that further facilitates the creation and running of auto-phylo pipelines. Inferences on S-RNase specificity, are critical for both cross-based breeding and for the establishment of pollination requirements. Therefore, as a test case, we develop an auto-phylo pipeline to identify amino acid sites under positive selection, that are, in principle, those determining S-RNase specificity, starting from both non-annotated Prunus genomes and sequences available in public databases., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0-6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.

This research work introduces a novel, nonintrusive method for the automatic identification of Smith-Magenis syndrome, traditionally studied through genetic markers. The method utilizes cepstral peak prominence and various machine learning techniques, relying on a single metric computed by the research group. The performance of these techniques is evaluated across two case studies, each employing a unique data preprocessing approach. A proprietary data "windowing" technique is also developed to derive a more representative dataset. To address class imbalance in the dataset, the synthetic minority oversampling technique (SMOTE) is applied for data augmentation. The application of these preprocessing techniques has yielded promising results from a limited initial dataset. The study concludes that the k-nearest neighbors and linear discriminant analysis perform best, and that cepstral peak prominence is a promising measure for identifying Smith-Magenis syndrome., Competing Interests: 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., (Copyright © 2024 Fernández-Ruiz, Núñez-Vidal, Hidalgo-delaguía, Garayzábal-Heinze, Álvarez-Marquina, Martínez-Olalla and Palacios-Alonso.)

Clinical interpretation of genetic variants in the context of the patient's phenotype is a time-consuming and costly process. In-silico analysis using in-silico prediction tools, and molecular modeling have been developed to predict the influence of genetic variants on the quality and/or quantity of the resulting translated protein, and in this way, to alert clinicians of disease likelihood in the absence of previous evidence. Our objectives were to evaluate the success rate of the in-silico analysis in predicting the disease-causing variants as pathogenic and the single-nucleotide variants as neutral, and to establish the reliability of in-silico analysis for determining pathogenicity or neutrality of von Willebrand factor gene-associated genetic variants. Using in-silico analysis, we studied pathogenicity in 31 disease-causing variants, and neutrality in 61 single-nucleotide variants from patients previously diagnosed as type 2 von Willebrand disease. Disease-causing variants and non-synonymous single-nucleotide variants were explored by in-silico tools that analyze the amino acidic sequence. Intronic and synonymous single-nucleotide variants were analyzed by in-silico methods that evaluate the nucleotidic sequence. We found a consistent agreement between predictions achieved by in-silico prediction tools and molecular modeling, both for defining the pathogenicity of disease-causing variants and the neutrality of single-nucleotide variants. Based on our results, the in-silico analysis would help to define the pathogenicity or neutrality in novel genetic variants observed in patients with clinical and laboratory phenotypes suggestive of von Willebrand disease., (© 2023 Wiley Periodicals LLC.)

This document analyzes a survey conducted in three geographical areas in Spain, focusing on centers for individuals with cerebral palsy (CP). The study aims to determine the adherence rate to recommended physical activity guidelines, assess if there is a decline in interest in physical activity over time, identify the stage at which this decline occurs, and explore potential mechanisms, tools, or strategies to sustain long-term engagement in regular physical activity for this population. The 36-item questionnaire comprises multiple-choice, open-ended, and Likert scale-type questions. Data were collected on physical activity frequency and duration, daily living activities, and demographics. Statistical analysis identified patterns and relationships between variables. Findings reveal that only a 17.6% meets the World Health Organization (WHO) recommendations regarding regular physical activity (RPA), decreasing in frequency or number of days a week, (3.7 d/w to 2.9 d/w; p < 0.01) and duration (50.5 min/d to 45.2 min/d; p < 0.001) with age, especially for those with higher Gross Motor Function Classification System (GMFCS) mobility levels. Obesity slightly correlates with session duration (ρ = -0.207; p < 0.05), not mobility limitations. Gender has no significant impact on mobility, communication, or physical activity, while age affects variables such as body mass index (BMI) and engagement ( p < 0.01). A substantial proportion follows regular physical activities based on health professionals' advice, with interest decreasing with age. To improve adherence, focusing on sports-oriented goals, group sessions, and games is recommended. These findings emphasize the importance of personalized programs, particularly for older individuals and those with greater mobility limitations.

In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.

SARS-CoV-2 amino acid variants that contribute to an increased transmissibility or to host immune system escape are likely to increase in frequency due to positive selection and may be identified using different methods, such as codeML, FEL, FUBAR, and MEME. Nevertheless, when using different methods, the results do not always agree. The sampling scheme used in different studies may partially explain the differences that are found, but there is also the possibility that some of the identified positively selected amino acid sites are false positives. This is especially important in the context of very large-scale projects where hundreds of analyses have been performed for the same protein-coding gene. To account for these issues, in this work, we have identified positively selected amino acid sites in SARS-CoV-2 and 15 other coronavirus species, using both codeML and FUBAR, and compared the location of such sites in the different species. Moreover, we also compared our results to those that are available in the COV2Var database and the frequency of the 10 most frequent variants and predicted protein location to identify those sites that are supported by multiple lines of evidence. Amino acid changes observed at these sites should always be of concern. The information reported for SARS-CoV-2 can also be used to identify variants of concern in other coronaviruses.

Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes. However, there are multiple risks and potentially unintended consequences associated with their use in healthcare applications. This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reduction in the human element of patient care. For the medical profession, risks include the impact on employment, changes in the patient-doctor dynamic, and the need for extensive training in both system operation and data interpretation., (© 2024. The Author(s).)

Parkinson's disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up.

In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields.

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process.

The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues.