Asymptotic monotonicity of the orthogonal speed and rate of convergence for semigroups of holomorphic self-maps of the unit disc

We show that the orthogonal speed of semigroups of holomorphic self-maps of the unit disc is asymptotically monotone in most cases. Such a theorem allows to generalize previous results of D. Betsakos and D. Betsakos, M. D. Contreras and S. D\'iaz-Madrigal and to obtain new estimates for the rate of convergence of orbits of semigroups.Comment: Final version. To appear in Rev. Mat. Iberoa

A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolu- tion of a given disease for the particular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to better estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of sam- ples available for a particular cancer type. To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been traditionally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of tradi- tional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset com- posed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a col- lection of specific breast cancer samples. This transfer-learning approach aims at combining supervised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Empowering Community Dwelling Older Citizens to Improve Their Balance with a Novel Technology Platform

The prevalence of balance deficits increases as the population is ageing. Such deficits are associated with the increased incidence of falls which in turn is linked with substantial limited functionality and morbidity. Vestibular rehabilitation therapy (VRT) as a component of the treatment has been shown to be effective in reducing symptoms and improving balance. HOLOBALANCE is an intervention based on a novel technology platform for providing VRT unsupervised, at home which means that motivating citizens to be compliant and promoting empowerment are the cornerstones for its wide adoption. Here we present how citizens empowerment is being addressed in HOLOBALANCE

A Dynamic Bayesian Network Approach to Behavioral Modelling of Elderly People during a Home-based Augmented Reality Balance Physiotherapy Programme

In this study, we propose a dynamic Bayesian network (DBN)-based approach to behavioral modelling of community dwelling older adults at risk for falls during the daily sessions of a hologram-enabled vestibular rehabilitation therapy programme. The component of human behavior being modelled is the level of frustration experienced by the user at each exercise, as it is assessed by the NASA Task Load Index. Herein, we present the topology of the DBN and test its inference performance on real-patient data.Clinical Relevance- Precise behavioral modelling will provide an indicator for tailoring the rehabilitation programme to each individual's personal psychological needs

Achieving adherence in home-based rehabilitation with novel human machine interactions that stimulate community-dwelling older adults

Balance disorders are expressed with main symptoms of vertigo, dizziness instability and disorientation. Most of them are caused by inner ear pathologies, but neurological, medical and psychological factors are also responsible. Balance disorders overwhelmingly affect daily activities and cause psychological and emotional hardship. They are also the main cause of falls which are a global epidemic. Home based balance rehabilitation is an effective approach for alleviating symptoms and for improving balance and self-confidence. However, the adherence in such programs is usually low with lack of motivation and disease related issues being the most influential factors. Holobalance adopts the Capability, Opportunity and Motivation (COM) and Behaviour (B) model to identify the sources of the behaviour that should be targeted for intervention and proposes specific Information Technology components that provide the identified interventions to the users in order to achieve the target behavioural change, which in this case is adherence to home base rehabilitation

Addressing the clinical unmet needs in primary Sjögren's Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs. © 2022 The Author(s

Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants’ later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant–environment interactions by elucidating plants’ temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant–environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity

A Replicated Network Approach to 'Big Data' in Ecology

International audienceGlobal environmental change is a pressing issue as evidenced by the rise of extreme weather conditions in many parts of the world, threatening the survival of vulnerable species and habitats. Effective monitoring of climatic and anthropogenic impacts is therefore critical to safeguarding ecosystems, and it would allow us to better understand their response to stressors and predict long-term impacts. Ecological networks provide a biomonitoring framework for examining the system-level response and functioning of an ecosystem, but have been, until recently, constrained by limited empirical data due to the laborious nature of their construction. Hence, most experimental designs have been confined to a single network or a small number of replicate networks, resulting in statistical uncertainty, low resolution, limited spatiotemporal scale and oversimplified assumptions. Advances in data sampling and curation methodologies, such as next-generation sequencing (NGS) and the Internet 'Cloud', have facilitated the emergence of the 'Big Data' phenomenon in Ecology, enabling the construction of ecological networks to be carried out effectively and efficiently. This provides to ecologists an excellent opportunity to expand the way they study ecological networks. In particular, highly replicated networks are now within our grasp if new NGS technologies are combined with machine learning to develop network building methods. A replicated network approach will allow temporal and spatial variations embedded in the data to be taken into consideration, overcoming the limitations in the current 'single network' approach. We are still at the embryonic stage in exploring replicated networks, and with these new opportunities we also face new challenges. In this chapter, we discuss some of these challenges and highlight potential approaches that will help us build and analyse replicated networks to better understand how complex ecosystems operate, and the services and functioning they provide, paving the way for deciphering ecological big data reliably in the future