Language disturbances are a core feature of schizophrenia, often studied as a formal thought disorder. The neurobiology of language in schizophrenia has been addressed within the same framework, that language and thought are equivalents considering symptoms and not signs. This review aims to systematically examine published peer-reviewed studies that employed neuroimaging techniques to investigate aberrant brain-language networks in individuals with schizophrenia in relation to linguistic signs.

This article introduces a novel systematic methodology for modeling a class of multidimensional linear mechanical systems that directly allows to obtain their infinite-dimensional port-Hamiltonian representation. While the approach is tailored to systems governed by specific kinematic assumptions, it encompasses a wide range of models found in current literature, including ℓ-dimensional elasticity models (where ℓ = 1, 2, 3), vibrating strings, torsion in circular bars, classical beam and plate models, among others. The methodology involves formulating the displacement field using primary generalized coordinates via a linear algebraic relation. The non-zero components of the strain tensor are then calculated and expressed using secondary generalized coordinates, enabling the characterization of the skew-adjoint differential operator associated with the port-Hamiltonian representation. By applying Hamilton's principle and employing a specially developed integration by parts formula for the considered class of differential operators, the port-Hamiltonian model is directly obtained, along with the definition of boundary inputs and outputs. To illustrate the methodology, the plate modeling process based on Reddy's third-order shear deformation theory is presented as an example. To the best of our knowledge, this is the first time that a port-Hamiltonian representation of this system is presented in the literature.

This study presents a benchmark of two physics-guided neural surrogates – PhyCNN and multi–LSTM – across three single-degree-of-freedom system scenarios of increasing nonlinearity: (I) a Duffing oscillator; (II) a hysteretic Bouc–Wen system under band-limited noise; and (III) a hysteretic Bouc–Wen isolator representative of base-isolated buildings subjected to long duration Chilean earthquakes. Physics-guided LSTMs achieve the lowest errors in displacement and velocity and better reproduce hysteretic geometry; CNNs yield tighter, more stable errors when the normalized internal force is predicted directly. Reconstructing force from LSTM velocity closes much of the gap but remains fragile in long, strongly nonlinear records due to residual-drift accumulation. Simple training discipline—input/target normalization, validation-driven early stopping and scheduling, and light force regularization—substantially improves robustness.

Quorum sensing governs bacterial communication, playing a crucial role in regulating population behaviour. We propose a mathematical model that uncovers chaotic dynamics within quorum sensing networks, highlighting challenges to predictability. The model explores interactions between autoinducers and two bacterial subtypes, revealing oscillatory dynamics in both a constant autoinducer sub-model and the full three-component model. In the latter case, we find that the complicated dynamics can be explained by the presence of homoclinic Shilnikov bifurcations. We employed a combination of normal form analysis and numerical continuation methods to analyse the system.

En esta tesis se realiza el desarrollo de un modelo de Reinforcement Learning para optimización secuencial de portafolios, incorporando el riesgo mediante el Expected Shortfall. El modelo aprende políticas de decisiones sobre activos de un portafolio (comprar, vender o mantener) usando información de retornos de distintos tipos de activos (acciones, ETFs y criptomonedas) para distintas líneas temporales, junto con un umbral de decisión compuesta que se adapta de forma dinámica según la volatilidad de los retornos. Basándose en prueba y error, obteniendo como recompensa una métrica de retorno ajustado por riesgo, definida por el retorno marginal obtenido de un conjunto de movimientos respecto a una inversión libre de riesgo, ajustada por una métrica de riesgo explícita.El objetivo principal de la tesis es evaluar si un enfoque de Reinforcement Learning logra detectar patrones dinámicos de mercado y lograr generalizarlos para escenarios fuera de muestra. Comparándolo con otros modelos tanto financieros estadísticos, como modelos de machine learning supervisados y no supervisados. Los resultados muestran que, para enfoques más conservadores sobre el umbral, el modelo de Reinforcement Learning tiene un menor gap entre entrenamiento validación y testeo, siendo sus resultados más permanentes entre fases. Los enfoques más de rentabilidad pura, poseen mayor volatilidad entre fases de validación y testeo, teniendo un mayor riesgo que sus competidores y resultados similares.

Piscirickettsia salmonis is the etiological agent of Piscirickettsiosis, one of the most severe and detrimental diseases affecting the Chilean salmon farming industry. Currently, there are 26 vaccines with provisional registration available for prophylactic use in salmonids. However, none have proven effective in fully controlling epidemic outbreaks under field conditions. Several researchers have identified two predominant genogroups along the Chilean coast, designated as LF-89 and EM-90, which exhibit a variable spatiotemporal distribution in southern Chilean salmon farms in recent years. Recent evidence suggests that the biological mechanisms of both genogroups may interact synergistically during infection, indicating the potential for co-infections in contemporary farms. This scenario further complicates vaccine efficacy, as many are designed to target only a predominant genogroup. Thus, the need arises to identify alternative prophylactic methods to ensure and contribute to the sustainability of the Chilean salmon farming industry. A novel prophylactic approach proposed in this thesis involves the use of recombinant nanoproteins, also known as inclusion bodies, with antigenic functions against Piscirickettsiosis. These inclusion bodies are aggregates of soluble and insoluble proteins with amyloid characteristics, eliminating the need for encapsulation. They are scalable, lyophilizable, and highly stable under adverse temperature and pH conditions. This thesis aims to evaluate the ability of certain antigenic protein inclusion bodies from P. salmonis to induce an adaptive immune response in Atlantic salmon (Salmo salar) and their potential use as immunogens in a vaccine prototype. To achieve this objective, three antigenic sequences with chimeric characteristics from the LF-89 and EM-90 genogroups of P. salmonis were designed and produced as recombinant inclusion bodies in E. coli BL21(DE3). The prototypes were designated for commercial protection purposes as SkipZ, PulseJ, and HopQ. These prototypes were quantified and characterized using techniques such as Western blot. Their uptake and immunostimulatory activity were evaluated in salmon macrophages through in vitro assays. Subsequently, juvenile salmon were vaccinated, and the effects of the SkipZ and HopQ prototypes were analyzed in terms of gene expression (qPCR) and immunoglobulin production (ELISA). The three prototypes achieved production efficiencies of 57 mg/L for PulseJ, 40 mg/L for SkipZ, and 26,8 mg/L for HopQ. These prototypes were internalized by salmon macrophages (RTS-11) with uptake efficiencies of 5% for SkipZ, 26% for PulseJ, and 54% for HopQ. In vitro assays showed activation at both transcriptional and translational levels of molecules associated with antigen presentation and pro-inflammatory markers, with notable activation capacities observed for HopQ and SkipZ at 20 µg/mL and 5 µg/mL, respectively. In vivo assays revealed that both vaccine prototypes, administered at doses of 0.5 mg/kg of fish for SkipZ and 2 mg/kg of fish for HopQ, robustly activated the Th1 response, with a marked increase in IFN-γ and IL-12 expression. This strong upregulation might suppress CD8+ lymphocyte differentiation, potentially reducing the effectiveness of the cellular immune response in fully eliminating the pathogen. However, this effect may contribute to maintaining homeostasis in fish during the exacerbated inflammation induced by the vaccines. Moreover, no significant change was observed in the Th2 response, suggesting that the humoral response was not predominantly activated. In conclusion, the vaccine prototypes based on antigenic sequences of Piscirickettsia salmonis and produced as chimeric inclusion bodies demonstrated potent immunogenicity, capable of inducing a robust adaptive immune response in Atlantic salmon. The activation of the Th1 response by HopQ and SkipZ was particularly noteworthy. However, further studies are needed to evaluate their protective effects in challenge trials with the pathogen, including both genogroups, and to determine their long-term effectiveness.

This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. The results demonstrate that our measure effectively distinguishes relevant from irrelevant attributes and highlights how relevance is influenced by noise, providing a more nuanced understanding compared to established methods such as Pearson, Spearman, Kendall, MIC, MAS, MEV, GMIC, and 𝑃ℎ𝑖𝑘 This research underscores the importance of phenomenon-centric explainability, reproducibility, and robust attribute relevance evaluation in the development of predictive models. By enhancing both the interpretability and contextual accuracy of models, our approach not only supports more informed decision making but also contributes to a deeper understanding of the underlying mechanisms in diverse application domains, such as biomedical research, financial modeling, astronomy, and others.

A critical element in understanding voice production mechanisms is the characterization of vocal fold collision, which is widely considered a primary etiological factor in the development of common phonotraumatic lesions such as nodules and polyps. This paper describes the development of a transoral, dual-sensor intraglottal/subglottal pressure probe for the simultaneous measurement of vocal fold collision and subglottal pressures during phonation using two miniature sensors positioned 7.6 mm apart at the distal end of a rigid cannula. Proof-of-concept testing was performed using excised whole-mount and hemilarynx human tissue aerodynamically driven into self-sustained oscillation, with systematic variation of the superior–inferior positioning of the vocal fold collision sensor. In the hemilarynx experiment, signals from the pressure sensors were synchronized with an acoustic microphone, a tracheal-surface accelerometer, and two high-speed video cameras recording at 4000 frames per second for top–down and en face imaging of the superior and medial vocal fold surfaces, respectively. As expected, the intraglottal pressure signal exhibited an impulse-like peak when vocal fold contact occurred, followed by a broader peak associated with intraglottal pressure build-up during the de-contacting phase. As subglottal pressure was increased, the peak amplitude of the collision pressure increased and typically reached a value below that of the average subglottal pressure. Results provide important baseline vocal fold collision pressure data with which computational models of voice production can be developed and in vivo measurements can be referenced.