Estudio y evaluación de métodos de visualización de imágenes biológicas multidimensionales utilizando OpenGL

Las modernas tecnologías de adquisición de imágenes (TAC, RMN, microscopia Confocal, etc.) permiten el registro, no sólo de imágenes tridimensionales, sino de imágenes multidimensionales. Sin embargo, el análisis visual de tales volúmenes de información no es una tarea simple, se requiere de programas de representación gráfica, que oculten la complejidad del sistema de digitalización facilitando el análisis al experto. En este trabajo se han seleccionado para la implementación, análisis y evaluación dos técnicas de representación de imágenes multidimensionales: Proyección de intensidades máximas y una variante de la misma en la cual se atenúa la intensidad de los objetos en función de la distancia de los mismos al observador. Se las implementó utilizando OpenGL vía texturas bidimensionales y texturas tridimensionales, se evaluaron cualitativamente la calidad de las representaciones, y se cuantificaron los tiempos de graficación. Finalmente, se analizaron las ventajas y desventajas del uso de las técnicas y de las herramientas disponibles para implementarlas en un programa.Sociedad Argentina de Informática e Investigación Operativ

A method for daily normalization in emotion recognition

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A method for daily normalization in emotion recognition

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A method for daily normalization in emotion recognition

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Audio recordings dataset of grazing jaw movements in dairy cattle.

This dataset is composed of correlated audio recordings and labels of ingestive jaw movements performed during grazing by dairy cattle. Using a wireless microphone, we recorded sounds of three Holstein dairy cows grazing short and tall alfalfa and short and tall fescue. Two experts in grazing behavior identified and labeled the start, end, and type of each jaw movement: bite, chew, and chew-bite (compound movement). For each segment of raw audio corresponding to a jaw movement we computed four well-known features: amplitude, duration, zero crossings, and envelope symmetry. These features are in the dataset and can be used as inputs to build automated methods for classification of ingestive jaw movements. Cow's grazing behavior can be monitored and characterized by identifying and analyzing these masticatory events

AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior

Leaf-cutter ants play a crucial role in agroecosystems, and understanding their behavior is key to developing effective damage control strategies. While several tracking solutions exist for ants in controlled environments or on aerial images, accurately measuring ant behavior in the wild remains a challenge. In this work, we propose a three-stage processing pipeline that segments individual ants, tracks their movement, and classifies whether they are carrying a leaf using a convolutional neural network. The output of the pipeline includes a timestamped record of the activity on the trail, accounting for parameters such as ant velocity, size and if it is going from or to the nest. We use the recently developed portable device AntVideoRecord to register video of a selected ant trail. To validate our approach, we collected a labeled dataset and evaluated each stage using standard metrics, achieving a median F2 score of 83% for segmentation, MOTA of 97% for tracking and F1 of 82% for detecting ants carrying a leaf. We then carried out a larger use case in the wild, demonstrating the effectiveness of our approach in capturing the intricate behaviors of leaf-cutter ants. We believe our method has the potential to inform the development of more effective ant damage control strategies in agroecosystems

Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches

MOTIVATION: In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated. RESULTS: In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches.This work was supported by ANPCyT (PICT 2018 3384, PICT 2018 2905, PICT 2019 3420) and UNL (CAI+D 2020 115). Researchers from sinc(i) and IAL are collaborating in the framework of the Program Science and Technology against Hunger, supported by the Argentinian Ministry of Science, to study and develop ncRNAs as exogenous bioactive molecules in agriculture. UC was funded by MICINN (IJC2019-039402-I). The work used computational resources from the Pirayu cluster, acquired with funds from the Santa Fe Science, Technology and Innovation Agency (ASACTEI), Project AC-00010-18, Res. No. 117/14. This equipment is part of the National High Performance Computing System of the Ministry of Science and Technology of Argentina. We also acknowledged the support of NVIDIA Corporation for the donation of GPUs used for this researchPeer Reviewed"Article signat per 12 autors/es: L A Bugnon, A A Edera, S Prochetto, M Gerard, J Raad, E Fenoy, M Rubiolo, U Chorostecki, T Gabaldón, F Ariel, L E Di Persia, D H Milone, G Stegmayer"Postprint (author's final draft

Audio recordings dataset of grazing jaw movements in dairy cattle.

This dataset is composed of correlated audio recordings and labels of ingestive jaw movements performed during grazing by dairy cattle. Using a wireless microphone, we recorded sounds of three Holstein dairy cows grazing short and tall alfalfa and short and tall fescue. Two experts in grazing behavior identified and labeled the start, end, and type of each jaw movement: bite, chew, and chew-bite (compound movement). For each segment of raw audio corresponding to a jaw movement we computed four well-known features: amplitude, duration, zero crossings, and envelope symmetry. These features are in the dataset and can be used as inputs to build automated methods for classification of ingestive jaw movements. Cow's grazing behavior can be monitored and characterized by identifying and analyzing these masticatory events

Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches

Motivation: In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated. Results: In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches.Fil: Bugnon, Leandro Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Edera, A. A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Prochetto, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Gerard, M.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Raad, Jonathan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Fenoy, E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rubiolo, M.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Chorostecki, U.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Gabaldón, T.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Ariel, Federico Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin