Soundscape Characterization Using Autoencoders and Unsupervised Learning.

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area's soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.

Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review.

Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.

Data-driven model optimization for optically pumped magnetometer sensor arrays.

Optically pumped magnetometers (OPMs) have reached sensitivity levels that make them viable portable alternatives to traditional superconducting technology for magnetoencephalography (MEG). OPMs do not require cryogenic cooling and can therefore be placed directly on the scalp surface. Unlike cryogenic systems, based on a well-characterised fixed arrays essentially linear in applied flux, OPM devices, based on different physical principles, present new modelling challenges. Here, we outline an empirical Bayesian framework that can be used to compare between and optimise sensor arrays. We perturb the sensor geometry (via simulation) and with analytic model comparison methods estimate the true sensor geometry. The width of these perturbation curves allows us to compare different MEG systems. We test this technique using simulated and real data from SQUID and OPM recordings using head-casts and scanner-casts. Finally, we show that given knowledge of underlying brain anatomy, it is possible to estimate the true sensor geometry from the OPM data themselves using a model comparison framework. This implies that the requirement for accurate knowledge of the sensor positions and orientations a priori may be relaxed. As this procedure uses the cortical manifold as spatial support there is no co-registration procedure or reliance on scalp landmarks.

Cognitive neuroscience using wearable magnetometer arrays: Non-invasive assessment of language function.

Recent work has demonstrated that Optically Pumped Magnetometers (OPMs) can be utilised to create a wearable Magnetoencephalography (MEG) system that is motion robust. In this study, we use this system to map eloquent cortex using a clinically validated language lateralisation paradigm (covert verb generation: 120 trials, ∼10 min total duration) in healthy adults (n = 3). We show that it is possible to lateralise and localise language function on a case by case basis using this system. Specifically, we show that at a sensor and source level we can reliably detect a lateralising beta band (15-30 Hz) desynchronization in all subjects. This is the first study of human cognition using OPMs and not only highlights this technology's utility as tool for (developmental) cognitive neuroscience but also its potential to contribute to surgical planning via mapping of eloquent cortex, especially in young children.

Identification and monitoring of brain activity based on stochastic relevance analysis of short-time EEG rhythms.

BACKGROUND: The extraction of physiological rhythms from electroencephalography (EEG) data and their automated analyses are extensively studied in clinical monitoring, to find traces of interictal/ictal states of epilepsy. METHODS: Because brain wave rhythms in normal and interictal/ictal events, differently influence neuronal activity, our proposed methodology measures the contribution of each rhythm. These contributions are measured in terms of their stochastic variability and are extracted from a Short Time Fourier Transform to highlight the non-stationary behavior of the EEG data. Then, we performed a variability-based relevance analysis by handling the multivariate short-time rhythm representation within a subspace framework. This maximizes the usability of the input information and preserves only the data that contribute to the brain activity classification. For neural activity monitoring, we also developed a new relevance rhythm diagram that qualitatively evaluates the rhythm variability throughout long time periods in order to distinguish events with different neuronal activities. RESULTS: Evaluations were carried out over two EEG datasets, one of which was recorded in a noise-filled environment. The method was evaluated for three different classification problems, each of which addressed a different interpretation of a medical problem. We perform a blinded study of 40 patients using the support-vector machine classifier cross-validation scheme. The obtained results show that the developed relevance analysis was capable of accurately differentiating normal, ictal and interictal activities. CONCLUSIONS: The proposed approach provides the reliable identification of traces of interictal/ictal states of epilepsy. The introduced relevance rhythm diagrams of physiological rhythms provides effective means of monitoring epileptic seizures; additionally, these diagrams are easily implemented and provide simple clinical interpretation. The developed variability-based relevance analysis can be translated to other monitoring applications involving time-variant biomedical data.

Economic cost of follicular cysts in postpartum dairy cows / Costo económico por quistes foliculares en vacas lecheras posparto / Custo econômico por cistos foliculares em vacas leiteiras pós-parto

Follicular cysts represent a common reproductive pathology in dairy cows. Prevention and early detection are crucial to prevent economic loses from medical treatments and decreased milk production. Objective: to evaluate the economic loses attributed to follicular cysts on post-partum dairy cows. Methods: Medical treatments and milk production records were retrieved from a total of 950 cows at five different herds in the high plains of Antioquia. Usual treatment consisted of 1-3 doses of GnRH and PGF2 . A sensitivity analysis was conducted to assess the cost per liter of milk produced depending on the response to treatment. Results and conclusion: The mean cost of milk production was 496 Colombian pesos/liter for cows free of cysts. This value increased 3.5, 13.3 and 33.8% for cystic cows that received one, two, or three hormonal treatments, respectively. Consequently, any measures aimed at reducing the incidence and rapid resolution of follicular cyst will result in substantial financial gains to the producer.

Considerando que los quistes foliculares son una patología reproductiva común en las vacas lecheras, y debido a que los costos asociados a tratamientos y bajas de producción suelen causar pérdidas económicas considerables, la prevención y el diagnóstico de éstos cobra un papel importante en la competitividad del productor. Objetivo: evaluar el efecto de los quistes foliculares en vacas posparto sobre el costo económico de la producción de leche. Métodos: el estudio incluyó 950 vacas de cinco hatos ubicados en el municipio de San Pedro de los Milagros, altiplano norte del departamento de Antioquia. A las vacas diagnosticadas con quistes, se les aplicó de una a tres dosis de GnRH y PGF2 según su respuesta al tratamiento luteinizante. Se realizó un análisis de sensibilidad para evaluar el costo de producción del litro de leche bajo los posibles escenarios de intervalo entre partos (IEP), calculado en función de la respuesta al tratamiento. Resultado y conclusión: el costo unitario de producción de leche ascendió en promedio a 496 COP/litro para vacas que no presentan quistes, valor que se incrementó en 3.5, 13.3 y 33.8%, ya que se hizo necesario aplicar de una a tres repeticiones del tratamiento respectivamente, por la no resolución del quiste; este aumento en los costos demuestran la importancia de un diagnóstico y tratamiento oportuno.

Considerando que os cistos foliculares são uma patologia reprodutiva comum na pecuária leiteira, e que os custos associados ao tratamento da doença e a diminuição da produção causam consideráveis perdas econômicas; a prevenção e o diagnóstico desta patologia têm um papel fundamental na competitividade do produtor. Objetivo: avaliar o efeito de cistos foliculares em vacas pós-parto sobre o custo econômico da produção leiteira. Métodos: 950 vacas foram estudadas em cinco rebanhos no município de San Pedro de los Milagros, planalto norte do departamento de Antioquia-Colômbia. Nas vacas que tinham cistos administrou-se de uma a três doses de GnRH e PGF2 segundo sua resposta ao tratamento luteinizante. Realizou-se uma análise de sensibilidade para avaliar o custo de produção do litro de leite, sob os possíveis cenários de intervalo entre partos (IEP), calculado em função da resposta ao tratamento. Resultados e conclusões: O custo unitário da produção de leite atingiu, em média, $496 COP (peso colombiano) por litro para as vacas que não têm cistos; o valor aumentou 3.5, 13.3 e 33.8%, quando se tornou necessário aplicar uma, duas ou três doses de GnRH e PGF2 . Estes aumentos nos custos demonstram a importância de um diagnostico e tratamento oportuno.