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Unveiling the Wing Shape Variation in Northern Altiplano Ecosystems: The Example of the Butterfly Phulia nymphula Using Geometric Morphometrics
No full textSimple Summary The Andean Altiplano, known for its extreme weather and high biodiversity, is an ideal place to study how insects adapt to their environment. This research focuses on the butterfly species Phulia nymphula, which is common in the high-altitudinal Andes Mountains, to identify how their wing shapes vary across six locations in the Northern Chilean Altiplano. By analyzing the wings of 77 butterflies, the study found significant differences in wing shape, likely due to local environmental conditions. These differences suggest that the butterflies have adapted to their specific habitats. The findings showed how the wing shape differentiate between localities across the Northern Altiplano and provide insights into how high-altitude species evolve and adapt through changes in their morphology, highlighting the role of ecological and evolutionary processes in shaping biodiversity in extreme environments. Abstract The Andean Altiplano, characterized by its extreme climatic conditions and high levels of biodiversity, provides a unique environment for studying ecological and evolutionary adaptations in insect morphology. Butterflies, due their large wing surface compared to body surface, and wide distribution among a geographical area given the flight capabilities provided by their wings, constitute a good biological model to study morphological adaptations following extreme weathers. This study focuses on Phulia nymphula, a butterfly species widely distributed in the Andes, to evaluate wing shape variation across six localities in the Northern Chilean Altiplano. The geometric morphometrics analysis of 77 specimens from six locations from the Chilean Altiplano (Caquena, Sorapata Lake, Chungará, Casiri Macho Lake, Surire Salt Flat, and Visviri) revealed significant differences in wing shape among populations. According to the presented results, variations are likely influenced by local environmental conditions and selective pressures, suggesting specific adaptations to the microhabitats of the Altiplano. The first three principal components represented 60.92% of the total wing shape variation. The detected morphological differences indicate adaptive divergence among populations, reflecting evolutionary responses to the extreme and fragmented conditions of the Altiplano. This study gives insights into the understanding of how high-altitude species can diversify and adapt through morphological variation, providing evidence of ecological and evolutionary processes shaping biodiversity in extreme environments
The revision of fossil big-eyed bugs suggests a peculiar evolutionary history of a peculiar true bug family (Heteroptera: Lygaeoidea: Geocoridae) (vol 103, pg 531, 2023)
No full textThere has to be a correction in the acknowledgements for figure 5: The photos in Figure 5 (Fig. 5. Geocoris (Geocoris) monserrati Ortuño and Arillo, 1997 (holotype): a overview of fossil; b dorsal half; c ventral half (scale bar = 1 mm for images b and c). were done by José-Carmelo Corral, PhD, external employee at the Museo de Ciencias Naturales de Álava (Alava Museum of Natural Sciences), Vitoria-Gasteiz, Spain. The authors declare, that they acted in good faith and were unaware that the photographer was not acknowledged. The authors apologise for any inconvenience and hereby express their gratitude to Dr. Corral for the photos of the types of G. monserrati
Exponential stabilization of a structural acoustic model arising in the control of noise
No full textWe consider a coupled hybrid system whose main application is the problem of the active control of noise. The model describes the interaction of acoustic vibrations in the interior of a given two-dimensional cavity with the mechanical vibrations of two damped strings located in a part of the boundary of the cavity, in which suitable feedbacks are acting. Our main result is that the total energy associated to this model decays exponentially as time goes to infinity
Emerging role of Metformin in Alzheimer's disease: A translational view
No full textAlzheimer's disease (AD) constitutes a major public-health issue of our time. Regrettably, despite our considerable understanding of the pathophysiological aspects of this disease, current interventions lead to poor outcomes. Furthermore, experimentally promising compounds have continuously failed when translated to clinical trials. Along with increased population ageing, Type 2 Diabetes Mellitus (T2DM) has become an extremely common condition, mainly due to unbalanced dietary habits. Substantial epidemiological evidence correlates T2DM with cognitive impairment as well. Considering that brain insulin resistance, mitochondrial dysfunction, oxidative stress, and amyloidogenesis are common phenomena, further approaching the common features among these pathological conditions. Metformin constitutes the first-choice drug to preclude insulin resistance in T2DM clinical management. Experimental evidence suggests that its functions might include neuroprotective effects, in addition to its hypoglycemic activity. This review aims to summarize and discuss current knowledge of experimental data on metformin on this path towards translational medicine. Finally, we discuss the controversial data of responses to metformin in vitro, and in vivo, animal models and human studies
Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides
No full textPeptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications
The Mangrove Restoration Tracker Tool: Meeting local practitioner needs and tracking progress toward global targets
No full textRestoration is a key component of global and national efforts to combat ecosystem degradation, reduce biodiversity loss, and adapt to climate change, and there is currently an impetus to scale up restoration efforts. However, our ability to track progress toward restoration targets is limited by the lack of consistent and standardized data on objectives, interventions, and outcomes. To address this, a collaboration of conservation practitioners and scientists from around the world have developed the Mangrove Restoration Tracker Tool (MRTT), an application to record and track outcomes from mangrove restoration projects. The MRTT records information across the lifetime of a project, capturing data describing the site background and pre-restoration baseline and the restoration interventions and costs, as well as post-restoration monitoring that incorporates both socioeconomic and ecological factors. The MRTT allows decision makers, practitioners, and site managers to access information that is essential in making informed, evidence-based decisions on restoration interventions to maximize impact and success
Linking people and riparian forests: a sociocultural and ecological approach to plan integrative restoration in farmlands
No full textGlobal initiatives to restore habitats aim to improve ecosystem health; however, restoration programs are challenged with balancing human needs with ecological restoration objectives. To advise programs that aim to restore forest in farmlands and complement other analyses on ecologically-based reference sites, we (1) identified species with sociocultural importance, termed as "priority species"; (2) developed an integrative index to find habitats where priority species coincide with healthy ecological conditions (i.e. relatively high diversity, specific plant composition, etc.); and (3) evaluated whether sociodemographic profiles of landowners influenced their plant knowledge and ecological condition of habitats. Our approach was applied to riparian forests in farmlands of the Tolten watershed in southern Chile. We conducted structured interviews to gather information on traditional uses and management of trees in riparian habitats from 45 landowners. We developed an integrative index by combining sociocultural information from interviews with existing vegetation data. From the list of 65 trees provided by landowners, we selected five priority species based on their high saliency, multiple uses, and known management. Only 6 out of 98 sites had high integrative index scores, with the majority showing low values for sociocultural and ecological conditions. Except for a difference in ecological criteria and gender, the evaluation of landowners' knowledge level with sociodemographic profiles did not show significant relationships. These findings suggest that our integrative index can guide the design of restoration objectives, emphasizing on species that are important to local communities by providing information on the ecological conditions in which these plants co-occur
How Did We Learn During the Pandemic? The Perception of University Students in Chile
No full textThe coronavirus (COVID-19) pandemic unleashed a series of changes in the way of teaching and learning at all educational levels. While remote teaching was the strategy used to guarantee the continuity of learning all over the world, we still lack of body of knowledge that would allow us to understand its implementation from the standpoint of the learner. With that in mind, this study sought to find out the variables that influence the perception of learning during the pandemic on the part of Chilean university students, as well as to characterize the barriers and facilitators of the learning process in remote education. A mixed methodology was applied, based on the analysis of an online survey of 1677 students in 34 Chilean universities (which included public and private ones as well as universities in both the capital and the provinces). The results show that the didactics and participation are variable predictors of the perception of learning on the part of the student body, and there are findings which suggest that key aspects in the design and implementation of processes of remote teaching should be taken into account
Self-Medication and Its Determinants in Health Professions Students at University of Magallanes, Chile
No full textIntroducción:
La automedicación se entiende como el consumo de medicamentos sin mediar la asesoría de un médico u otro profesional legalmente habilitado para prescribirlos. Aunque esta práctica puede implicar beneficios sanitarios, al realizarse irresponsablemente puede constituir importantes riesgos.
Objetivos:
Describir la conducta de los estudiantes de carreras profesionales del área de la salud de la Universidad de Magallanes en relación con el uso de medicamentos, y los determinantes que promueven la automedicación y la influencia del proceso formativo en la toma de decisiones.
Métodos:
Se desarrolló un estudio transversal de tipo descriptivo, en el que 303 estudiantes participaron de forma voluntaria y anónima, mediante una encuesta autoadministrada. El instrumento para la recabada de datos se validó a través de una prueba piloto y revisión por expertos.
Resultados:
La prevalencia de automedicación se situó en el 96,7 %, aunque con una frecuencia baja (menos de una vez al mes) en el 58,4 % de los casos. La falta de tiempo para acudir al médico (42,3 %) es la razón más citada para justificar la conducta y el principal signo o síntoma mencionado como detonante corresponde a los dolores de cabeza (81,6 %). Aunque el 90,1 % de los encuestados considera la automedicación como una práctica riesgosa, un 35,5 % considera poseer los conocimientos suficientes para un consumo responsable.
Conclusiones:
Existe una alta prevalencia de automedicación en la población universitaria, por lo que es necesario identificar oportunidades en el proceso formativo que permitan optimizar un uso racional de medicamentos en sí mismos y en la población beneficiaria de sus servicios
Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides
No full textWith their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery