Information Retrieval systems benefit from the use of long queries containing a large volume of search-relevant information. This situation is not common, as users of such systems tend to use very short and precise queries with few keywords. In this work we propose a modification of the Latent Dirichlet Allocation (LDA) technique using data from the document collection and its vocabulary for a better representation of short queries. Additionally, a study is carried out on how the modification of the proposed LDA weighted vectors increase the performance using relevant documents as feedback. The work shown in this paper is tested using three biomedical corpora (TREC Genomics 2004, TREC Genomics 2005 and OHSUMED) and one legal corpus (FIRE 2017). Results prove that the application of the proposed representation technique, as well as the feedback adjustment, clearly outperforms the baseline methods (BM25 and non-modified LDA).

Image generative models have advanced in many areas to produce synthetic images of high resolution and detail. This success has enabled its use in the biomedical field, paving the way for the generation of videos showing the biological evolution of its content. Despite the power of generative video models, their use has not yet extended to time-based development, focusing almost exclusively on generating motion in space. This situation is largely due to the lack of specific datasets and metrics to measure the individual quality of videos, particularly when there is no ground truth available for comparison. We propose a new dataset, called GoldenDOT , which tracks the evolution of apples cut in parallel over 10 days, allowing to observe their progress over time while remaining static. In addition, four new metrics are proposed that provide different analyses of the generated videos as a whole and individually. In this article, the proposed dataset and measures are used to study three state-of-the-art video generative models and their feasibility for video generation with biological development: Temporal GAN version 2 (TGANv2), Low-Dimensional Video Discriminator Generative Adversarial Network (LDVDGAN), and Video Diffusion Model (VDM). Among them, the TGANv2 model has managed to obtain the best results in most metrics, including those already known in the state of the art, demonstrating the viability of the new proposed metrics and their congruence with these standard measures.