Estratificación de la información ambiental y construcción de bioindicadores: identificación de áreas prioritarias para la conservación de la biodiversidad.

Las regiones naturales de la provincia de Córdoba presentan diversos ecosistemas que están afectados por un constante deterioro ambiental siendo degradadas por la deforestación, el avance de la frontera agropecuaria, los incendios rurales, la introducción de plantas exóticas, el sobre-pastoreo, las sequías prolongadas, y el crecimiento urbano de las villas turísticas. La biodiversidad permite que los ecosistemas tengan mayor resiliencia ante cambios climáticos o antrópicos, siendo de suma importancia para mantener los servicios ecosistémicos. La experiencia adquirida por el equipo de trabajo del CREAN en el monitoreo de sitios pilotos a través del proyecto LADA/FAO mediante la metodología WOCAT, para la evaluación de la degradación de la tierra y los usos de la tierra sirve de antecedente tecnológico para encarar este proyecto multidisciplinario en red. El producto a obtener es una cartografía multicapa sobre los usos de la tierra (LUS), en la región de las Sierras Chicas de Córdoba en donde queden delimitadas las áreas naturales conservadas, las zonas explotadas con un uso sustentable, las áreas degradadas con procesos de desertificación, el estado de las cuencas proveedoras de agua a ríos y embalses, como así también las zonas urbanas y de explotaciones agropecuarias o mineras. Sobre la base de esta evaluación de bioindicadores y estratificación de la información ambiental se puede proveer de información a decisores sociales y políticos para que establezcan medidas que contribuyan a realizar acciones que protejan el ambiente, promuevan el manejo sustentable de las tierras y establezcan normativas para la conservación del suelo, el agua y la biodiversidad

Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea detection, such as airflow and electrocardiogram. However, the reduction of airflow normally leads to a decrease in the blood oxygen saturation level (SpO2). This signal is usually measured by a pulse oximeter, a sensor that is cheap, portable, and easy to assemble. Therefore, the SpO2 was chosen as the reference signal. Feature based classifiers with shallow neural networks have been developed to provide apnea detection using SpO2. However, two main issues arise, the need for feature creation and the selection of the more relevant features. Deep neural networks can solve these issues by employing featureless methods. Among multiple deep classifiers that have been developed, convolution neural networks (CNN) are gaining popularity. However, the selection of the CNN structure and hyperparameters are typically done by experts, where prior knowledge is essential. With these problems in mind, an algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal. Three different input sizes and databases were tested, and the best model achieved an average accuracy, sensitivity, and specificity of 94%, 92%, and 96%, respectively.info:eu-repo/semantics/publishedVersio

A Systematic Review of Detecting Sleep Apnea Using Deep Learning

Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.info:eu-repo/semantics/publishedVersio

A portable wireless device for cyclic alternating pattern estimation from an EEG monopolar derivation

Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.info:eu-repo/semantics/publishedVersio

An oximetry based wireless device for sleep apnea detection

Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.info:eu-repo/semantics/publishedVersio

On the Use of Kullback–Leibler Divergence for Kernel Selection and Interpretation in Variational Autoencoders for Feature Creation

This study presents a novel approach for kernel selection based on Kullback–Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of latent variables to reduce the model’s parameters. Each latent variable is sampled from the distribution associated with a single kernel of the last encoder’s convolutional layer, resulting in an individual distribution for each kernel. Relevant features are selected from the sampled latent variables to perform kernel selection, which filters out uninformative features and, consequently, unnecessary kernels. Both the proposed filter method and the sequential feature selection (standard wrapper method) were examined for feature selection. Particularly, the filter method evaluates the Kullback–Leibler divergence between all kernels’ distributions and hypothesizes that similar kernels can be discarded as they do not convey relevant information. This hypothesis was confirmed through the experiments performed on four standard datasets, where it was observed that the number of kernels can be reduced without meaningfully affecting the performance. This analysis was based on the accuracy of the model when the selected kernels fed a probabilistic classifier and the feature-based similarity index to appraise the quality of the reconstructed images when the variational autoencoder only uses the selected kernels. Therefore, the proposed methodology guides the reduction of the number of parameters of the model, making it suitable for developing applications for resource-constrained devices.info:eu-repo/semantics/publishedVersio

Echium acanthocarpum hairy root cultures, a suitable system for polyunsaturated fatty acid studies and production

<p>Abstract</p> <p>Background</p> <p>The therapeutic and health promoting role of highly unsaturated fatty acids (HUFAs) from fish, <it>i.e. </it>eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3) are well known. These same benefits may however be shared by some of their precursors, the polyunsaturated fatty acids (PUFAs), such as stearidonic acid (SDA, 18:4 n-3). In order to obtain alternative sources for the large-scale production of PUFAs, new searches are being conducted focusing on higher plants oils which can contain these n-3 and n-6 C18 precursors, <it>i.e. </it>SDA and GLA (18:3n-6, γ-linolenic acid).</p> <p>Results</p> <p>The establishment of the novel <it>Echium acanthocarpum </it>hairy root cultures represents a powerful tool in order to research the accumulation and metabolism of fatty acids (FAs) in a plant particularly rich in GLA and SDA. Furthermore, this study constitutes the first example of a <it>Boraginaceae </it>species hairy root induction and establishment for FA studies and production. The dominant PUFAs, 18:2n-6 (LA, linoleic acid) and 18:3n-6 (GLA), accounted for about 50% of total FAs obtained, while the n-3 PUFAs, 18:3n-3 (ALA, α-linolenic acid) and 18:4n-3 (SDA), represented approximately 5% of the total. Production of FAs did not parallel hairy root growth, and the optimal productivity was always associated with the highest biomass density during the culture period. Assuming a compromise between FA production and hairy root biomass, it was determined that sampling times 4 and 5 gave the most useful FA yields. Total lipid amounts were in general comparable between the different hairy root lines (29.75 and 60.95 mg/g DW), with the major lipid classes being triacylglycerols. The FAs were chiefly stored in the hairy roots with very minute amounts being released into the liquid nutrient medium.</p> <p>Conclusions</p> <p>The novel results presented here show the utility and high potential of <it>E. acanthocarpum </it>hairy roots. They are capable of biosynthesizing and accumulating a large range of polyunsaturated FAs, including the target GLA and SDA fatty acids in appreciable quantities.</p

Introducción y evaluación de especies y cultivares forrajeros para áreas agro-ecológicamente desfavorecidas de la provincia de Córdoba (Programa: sustentabilidad productiva de pequeños rumiantes en áreas desfavorables - supprad)

Las regiones naturales de la provincia de Córdoba presentan diversos ecosistemas que están afectados por un constante deterioro ambiental siendo degradadas por la deforestación, el avance de la frontera agropecuaria, los incendios rurales, la introducción de plantas exóticas, el sobre-pastoreo, las sequías prolongadas, y el crecimiento urbano de las villas turísticas. La biodiversidad permite que los ecosistemas tengan mayor resiliencia ante cambios climáticos o antrópicos, siendo de suma importancia para mantener los servicios ecosistémicos. La experiencia adquirida por el equipo de trabajo del CREAN en el monitoreo de sitios pilotos a través del proyecto LADA/FAO mediante la metodología WOCAT, para la evaluación de la degradación de la tierra y los usos de la tierra sirve de antecedente tecnológico para encarar este proyecto multidisciplinario en red. El producto a obtener es una cartografía multicapa sobre los usos de la tierra (LUS), en la región de las Sierras Chicas de Córdoba en donde queden delimitadas las áreas naturales conservadas, las zonas explotadas con un uso sustentable, las áreas degradadas con procesos de desertificación, el estado de las cuencas proveedoras de agua a ríos y embalses, como así también las zonas urbanas y de explotaciones agropecuarias o mineras. Sobre la base de esta evaluación de bioindicadores y estratificación de la información ambiental se puede proveer de información a decisores sociales y políticos para que establezcan medidas que contribuyan a realizar acciones que protejan el ambiente, promuevan el manejo sustentable de las tierras y establezcan normativas para la conservación del suelo, el agua y la biodiversidad.Fil: Catalini, Carlos Gastón. Universidad Católica de Córdoba. Facultad de Ingeniería; Argentin

Towards automatic EEG cyclic alternating pattern analysis: a systematic review

This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical applica tion? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmationinfo:eu-repo/semantics/publishedVersio

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.info:eu-repo/semantics/publishedVersio