BUSCADOR RECOLECTA

Polymicrobial infections promote the appearance of a network of interactions that can lead to an increase in their antimicrobial tolerance or to the evasion of the host immune system. Pseudomonas aeruginosa and Burkholderia cenocepacia are two multidrug-resistant opportunistic pathogens that significantly influence host health and alter their antibiotic response when in coinfection. Using Galleria mellonella larvae as a model, we examined infection dynamics, immune responses, and antibiotic efficacy in single and coinfections combining acute and chronic P. aeruginosa strains with B. cenocepacia. Larval survival and bacterial dissemination were monitored, revealing tissue-specific infection patterns through confocal microscopy. Larval immunity, bacterial virulence gene expressions and antibiotic susceptibility were also analyzed to characterize host-pathogen dynamics. Our findings revealed that coinfections increased larval lethality and worsened overall health, with B. cenocepacia suppressing melanization and immune responses. Moreover, acute and chronic P. aeruginosa strains increase their virulence gene expression in coinfections. In terms of treatment, an increase of antibiotic susceptibility was observed in coinfected groups compared to single-infections. This study advances understanding of host-pathogen interactions in polymicrobial infections and highlights the need for improved therapeutic strategies.

Indian Art Music Melodic Similarity Dataset comprises audio excerpts and manually done annotations of the melodic phrases in Carnatic and Hindustani music. This dataset can be used to develop and evaluate approaches for computing melodic similarity between short-time melodic patterns in Indian art music. This dataset is divided into two parts, one for Carnatic music (CMD), and the other for Hindustani music (HMD). For more information about the dataset we refer to Chapter 3 of this thesis: http://hdl.handle.net/10803/398984.

The Carnatic Varnam Dataset is a collection of 28 solo vocal recordings of varnams in seven rāgas, performed by five trained singers. Each recording includes annotations of tāla cycles and machine-readable notations aligned with time. The dataset is designed to facilitate research in melodic analysis, intonation characterization, motif discovery, and audio-score alignment in Carnatic music.

Aquesta base de dades conté els resultats de l'instrument adminstrat en el projecte "Corporacions tecnològiques, plataformes educatives digitals i garantia dels drets de la infància amb enfocament de gènere" desenvolupat pel grup d'investigació ESBRINA​ (2017 SGR 1248) i las Asociaciones Federadas de Familias de Alumnos de Cataluña (aFFaC), i finançat per l'Agència Catalana de Cooperació al Desenvolupament (ACCD). S'hi recullen, sense editar, les 2910 respostes obtingudes., Esta base de datos contiene los resultados del instrumento administrado en el proyecto "Corporaciones tecnológicas, plataformas educativas digitales y garantía de los derechos de la infancia con enfoque de género" desarrollado por el grupo de Investigación ESBRINA​ (2017 SGR 1248) y les Asociaciones Federadas de Familias de Alumnos de Cataluña (aFFaC), y financiado por la Agencia Catalana de Cooperación al Desarrollo (ACCD). En esta base se recogen, sin editar, las 2910 respuestas obtenidas., This database contains the results of the project "Technology corporations, digital educational platforms and guarantee of children’s rights with a gender perspective" developed by the research group ESBRINA (2017 SGR 1248) and the Federated Associations of Student Families of Catalonia (aFFaC), and funded by the Catalan Agency for Development Cooperation (ACCD). This database contains, without editing, the 2910 responses obtained.

The Indian Art Music Raga Recognition Dataset comprises two sizable datasets, one for each music tradition: Carnatic Music Dataset (CMD) and Hindustani Music Dataset (HMD). These datasets include full-length audio recordings and their associated rāga labels. They are intended for developing and evaluating approaches for automatic rāga recognition in Indian art music. This repository contains the metadata and computed features for the dataset. To access the audio recordings, please refer to the corresponding Zenodo entry and submit your request.

The Jingju a Cappella Recordings Collection (JaCRC) is part of the Jingju Music Corpus created in the CompMusic project at the Music Technology Group, Universitat Pompeu Fabra, Barcelona (MTG). The JaCRC was created for different research tasks, mostly concerning melodic characteristics of jingju arias and pronunciation in jingju, and parts of the collection have been used in several publications. The JaCRC contains 314 recordings of jingju a cappella singing, plus 76 recordings of the jinghu accompaniment for their corresponding vocal tracks. Except for 53 of them, all of the recordings were newly created for this collection. The JaCRC also contains the manual segmentation of 217 vocal recordings and lyrics files for 156, 67 of which include annotations for start and end of each lyrics line in a related music score.

The Good-sounds dataset includes monophonic audio recordings of two types of musical exercises: single notes and scales. These recordings, performed by 15 professional musicians on flute, clarinet, trumpet, violin, and cello, were made in studio conditions at the Universitat Pompeu Fabra. The dataset supports research in sound quality, music education, and audio classification. It includes audio files in .wav format and metadata in .csv format detailing the exercise type, note, pitch, instrument, and performer, among others.

We provide a deep-learning method to better estimate the size of partially occluded apples. The method is based on ORCNN (https://github.com/waiyulam/ORCNN) and sizecnn (https://git.wur.nl/blok012/sizecnn), which extended Mask R-CNN network to simultaneously perform modal and amodal instance segmentation. The amodal mask is used to estimate the fruit diameter in pixels, while the modal mask is used to measure in the depth map the distance between the detected fruit and the camera and calculate the fruit diameter in mm by applying the pinhole camera model.

Using an in vitro colonic digestion model, we explored the prebiotic potential of dietary fibre concentrates (DFCs) derived from artichoke, red pepper, cucumber, and carrot byproducts. In vitro colonic digestions were carried out for up to 48 hours to simulate colonic digestion. Gut microbiota were assessed at 24 and 48 hours using quantitative PCR targeting key microbial taxa, including Firmicutes, Bacteroidetes, Clostridium, Bifidobacterium, Enterococcus, Lactobacillus, Bacteroidaceae, and Enterobacteriaceae. DNA was extracted using the QIAamp Fast DNA Stool Mini Kit and quantified using a NanoDrop spectrophotometer. Microbial abundance was normalized to total Eubacteria and calculated using the 2^ÐDelta-Delta Ct method. In parallel, Short-Chain Fatty Acids (SCFAs) productions (acetic, propionic, and butyric acids) were assed at 3, 6, 12, 18, 24, and 48 hours using gas chromatography coupled with flame ionization. Total SCFA concentrations and SCFA profiles were calculated to evaluate microbial metabolic activity in response to the different DFCs. Additionally, pH values were also recorded over time, using a pH meter.

Multitask Deep Neural Network for Fruit Detection and Regresion of Fruit Diameters in RGB-D images (based on Detectron2). This project is an extension of the MaskRCNN architecture that allows to compute the diameter of fruits along with performing instance segmentation. The baseline for this project has been the detectron2 implementation of the MaskRCNN (https://github.com/facebookresearch/detectron2).