1,721,013 research outputs found
A new feature selection strategy for K-mers sequence representation
No full textDNA sequence decomposition into k-mers (substrings of length k) and their frequency counting, defines a mapping of a sequence into a numerical space by a numerical feature vector of fixed length. This simple process allows to compute sequence comparison in an alignment free way, using common similarities and distance functions on the numerical codomain of the mapping. The most common used decomposition uses all the substrings of length k making the codomain of exponential dimension. This obviously can affect the time complexity of the similarity computation, and in general of the machine learning algorithm used for the purpose of sequence classification. Moreover, the presence of possible noisy features can also affect seriously the classification accuracy. In this paper we propose a feature selection method able to select the most informative k-mers associated to a set of DNA sequences. Such selection is
based on the Motif Independent Measure (MIM), an unbiased quantitative measure for DNA sequence specificity that we have recently introduced in the literature. Results
computed on three public datasets using the Support vector machine classifier, show the effectiveness of the proposed feature selection metho
Preliminary Nose Landing Gear Digital Twin for Damage Detection
No full textAn increase in aircraft availability and readiness is one of the most desired characteristics of aircraft fleets. Unforeseen failures cause additional expenses and are particularly critical when thinking about combat jets and Unmanned Aerial Vehicles (UAVs). For instance, these systems are used under extreme conditions, and there can be situations where standard maintenance procedures are impractical or unfeasible. Thus, it is important to develop a Health and Usage Monitoring System (HUMS) that relies on diagnostic and prognostic algorithms to minimise maintenance downtime, improve safety and availability, and reduce maintenance costs. In particular, within the realm of aircraft structures, landing gear emerges as one of the most intricate systems, comprising several elements, such as actuators, shock absorbers, and structural components. Therefore, this work aims to develop a preliminary digital twin of a nose landing gear and implement diagnostic algorithms within the framework of the Health and Usage Monitoring System (HUMS). In this context, a digital twin can be used to build a database of signals acquired under healthy and faulty conditions on which damage detection algorithms can be implemented and tested. In particular, two algorithms have been implemented: the first is based on the Root-Mean-Square Error (RMSE), while the second relies on the Mahalanobis distance (MD). The algorithms were tested for three nose landing gear subsystems, namely, the steering system, the retraction/extraction system, and the oleo-pneumatic shock absorber. A comparison is made between the two algorithms using the ROC curve and accuracy, assuming equal weight for missed detections and false alarms. The algorithm that uses the Mahalanobis distance demonstrated superior performance, with a lower false alarm rate and higher accuracy compared to the other algorithm
Applications of alignment-free methods in epigenomics
No full textEpigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic
patterns are established and maintained remains poorly understood. Recent studies have supported a role
of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify
distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here,
we review recent advances in such applications, including the methods to map DNA sequence to feature space,
sequence comparison and prediction models. Computational studies using these methods have provided important
insights into the epigenetic regulatory mechanisms
Dinamiche in famiglie con figli affetti da diverse forme di degenerazioni tapeto retiniche ereditarie
No full textPoster presentato al 22-esimo Congresso Nazionale di Oftalmologia, Padova, 23 giugno 200
Application of (lamellar) keratoplasty and limbal stem cell transplantation for corneal clouding in the mucopolysaccharidoses - a review
No full textCorneal clouding or opacification is a prominent feature of mucopolysaccharidosis (MPS), particularly in MPS I and VI. In patients with marked corneal clouding and visual impairment, penetrating keratoplasty may be considered to improve the patient's vision, functional capacity and quality of life. In MPS, glycosaminoglycans mainly accumulate in the corneal stroma and not in Descemet's membrane or the endothelium, therefore deep anterior lamellar keratoplasty (DALK) may be preferred in these patients over penetrating keratoplasty. Although there are only limited data on the use of DALK in MPS (I and VI) patients, the results are generally favourable. Nonetheless, when deciding on whether to perform keratoplasty in patients with MPS, the risk of general anaesthesia due to potential concomitant cardio-pulmonary problems and cervical spine instability, the potential presence of other ocular manifestations that also impair vision (e.g. glaucoma, retinal degeneration and optic atrophy) and/or complications such as allograft rejection and the risk of re-opacification of the graft, all need to be taken into consideration. Limbal stem cell transplantation, which can be combined with keratoplasty, also holds potential promise in the treatment of these complex cases. A review of the indications, local and systemic risks, techniques of lamellar and penetrating keratoplasty, and the potential of limbal stem cell transplantation is provided in the context of corneal opacity in MPS
Exploring transfer learning for improving ultrasonic guided wave-based damage localization
Full text linkDesigning maintenance strategies to reduce the failure risk of plated structures is paramount for increasing the safety level of aerospace, civil and mechanical systems. Although traditional time-scheduled maintenance policies are reliable, they come with costly operations and avoidable downtimes. Recently, more complex condition-based strategies have been studied in the literature. This class of maintenance actions rely on structural health monitoring (SHM) frameworks: a sensor network is installed on the structure diagnostic data are processed to monitor the health state of the structure. The high dimensionality of data and the limitations of model-based SHM algorithms have led researchers to investigate data-driven solutions for improving the reliability of condition-based strategies. So far, supervised machine learning strategies have mainly been considered. However, since the cost of generating labeled datasets usually turns out to be prohibitive, two alternative solutions have gained attention: unsupervised methods and transfer learning (TL). While the former approach has been proved to provide satisfactory damage detection performance, it requires external knowledge sources to also localize and quantify damage. Instead, transfer learning could be used for performing all the damage diagnosis tasks, without the need for coupling the data-driven method with complex algorithms to restore the information lost by using smaller datasets for training. TL allows adapting pre-trained ML tools to new situations, new tasks and new environments. Moreover, TL can be leveraged when few labeled data are available, or to adapt efficient tools that have already been trained on a slightly different task. In this work, TL and convolutional neural networks (CNNs) were leveraged for performing damage localization in composite plated structures. That is, domain adaptation and fine-tuning were used to make an in-house CNN-based framework for localizing structural damage flexible enough to work in different domains
Physics-Informed Machine Learning for Structural Damage Diagnosis in Aluminium Plates
Full text linkDamage diagnosis plays a crucial role in Structural Health Monitoring (SHM) by facilitating the identification, localization, and estimation of the extent of defects in structures. Lamb waves, known for their sensitivity to defects, are widely employed in SHM methods for thin-walled structures. Most of those traditional methods require extracting damage indices from Lamb wave signals. This operation involves substantial post-processing and implies that part of the diagnostic information is lost. To solve those limitations and improve the damage diagnosis accuracy, machine learning methods have recently been proposed in the literature. However, the reluctance of the industrial sector to adopt conventional black-box models due to their lack of explainability poses a challenge. This study proposes a physics-informed machine-learning approach to address the limitations of standard black-box methods. Particularly, a Physics-Informed Neural Network (PINN) is implemented to predict the density of an aluminium plate based on measurements of plate displacements caused by Lamb wave excitation. This is made possible by the implementation of a specific loss function, which leverages physical knowledge in the form of the partial differential equation governing Lamb waves. Predicting the plate density based on measured displacements eliminates the need for artificial damage indices, utilizing the density variation itself to detect and localize damage. Additionally, the outputs of the PINN, rooted in physics equations, offer enhanced explainability compared to standard black-box models. The versatility of this framework extends to predicting material properties distributions for components, and efforts will be directed towards adapting the method for composite materials, where the approach may pose additional challenges
A New Feature Selection Methodology for K-mers Representation of DNA Sequences
No full textDNAsequence decomposition into k-mers and their frequency counting, defines a mapping of a sequence into a numerical space by a numerical feature vector of fixed length. This simple process allows to compare sequences in an alignment free way, using common similarities and distance functions on the numerical codomain of the mapping. The most common used decomposition uses all the substrings of a fixed length k making the codomain of exponential dimension. This obviously can affect the time complexity of the similarity computation, and in general of the machine learning algorithm used for the purpose of sequence analysis.Moreover, the presence of possible noisy features can also affect the classification
accuracy. In this paper we propose a feature selection method able to select the most informative k-mers associated to a set of DNA sequences. Such selection is based on the Motif Independent Measure (MIM), an unbiased quantitative measure for DNA sequence specificity that we have recently introduced in the literature. Results computed on public
datasets show the effectiveness of the proposed feature selection metho
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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