Endomapper dataset of complete calibrated endoscopy procedures.

Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures is needed. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, making secondary use of medical data. Its main purpose is to facilitate the development and evaluation of Visual Simultaneous Localization and Mapping (VSLAM) methods in real endoscopy data. The dataset contains more than 24 hours of video. It is the first endoscopic dataset that includes endoscope calibration as well as the original calibration videos. Meta-data and annotations associated with the dataset vary from the anatomical landmarks, procedure labeling, segmentations, reconstructions, simulated sequences with ground truth and same patient procedures. The software used in this paper is publicly available.

Event Transformer +. A Multi-Purpose Solution for Efficient Event Data Processing.

Event cameras record sparse illumination changes with high temporal resolution and high dynamic range. Thanks to their sparse recording and low consumption, they are increasingly used in applications such as AR/VR and autonomous driving. Current top-performing methods often ignore specific event-data properties, leading to the development of generic but computationally expensive algorithms, while event-aware methods do not perform as well. We propose Event Transformer +, that improves our seminal work EvT with a refined patch-based event representation and a more robust backbone to achieve more accurate results, while still benefiting from event-data sparsity to increase its efficiency. Additionally, we show how our system can work with different data modalities and propose specific output heads, for event-stream classification (i.e. action recognition) and per-pixel predictions (dense depth estimation). Evaluation results show better performance to the state-of-the-art while requiring minimal computation resources, both on GPU and CPU.

A Self-Adaptive Gallery Construction Method for Open-World Person Re-Identification.

Person re-identification, or simply re-id, is the task of identifying again a person who has been seen in the past by a perception system. Multiple robotic applications, such as tracking or navigate-and-seek, use re-identification systems to perform their tasks. To solve the re-id problem, a common practice consists in using a gallery with relevant information about the people already observed. The construction of this gallery is a costly process, typically performed offline and only once because of the problems associated with labeling and storing new data as they arrive in the system. The resulting galleries from this process are static and do not acquire new knowledge from the scene, which is a limitation of the current re-id systems to work for open-world applications. Different from previous work, we overcome this limitation by presenting an unsupervised approach to automatically identify new people and incrementally build a gallery for open-world re-id that adapts prior knowledge with new information on a continuous basis. Our approach performs a comparison between the current person models and new unlabeled data to dynamically expand the gallery with new identities. We process the incoming information to maintain a small representative model of each person by exploiting concepts of information theory. The uncertainty and diversity of the new samples are analyzed to define which ones should be incorporated into the gallery. Experimental evaluation in challenging benchmarks includes an ablation study of the proposed framework, the assessment of different data selection algorithms that demonstrate the benefits of our approach, and a comparative analysis of the obtained results with other unsupervised and semi-supervised re-id methods.

Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera.

Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set.

Structure of the Mycobacterium tuberculosis D-alanine:D-alanine ligase, a target of the antituberculosis drug D-cycloserine.

D-alanine:D-alanine ligase (EC 6.3.2.4; Ddl) catalyzes the ATP-driven ligation of two D-alanine (D-Ala) molecules to form the D-alanyl:D-alanine dipeptide. This molecule is a key building block in peptidoglycan biosynthesis, making Ddl an attractive target for drug development. D-Cycloserine (DCS), an analog of D-Ala and a prototype Ddl inhibitor, has shown promise for the treatment of tuberculosis. Here, we report the crystal structure of Mycobacterium tuberculosis Ddl at a resolution of 2.1 Å. This structure indicates that Ddl is a dimer and consists of three discrete domains; the ligand binding cavity is at the intersection of all three domains and conjoined by several loop regions. The M. tuberculosis apo Ddl structure shows a novel conformation that has not yet been observed in Ddl enzymes from other species. The nucleotide and D-alanine binding pockets are flexible, requiring significant structural rearrangement of the bordering regions for entry and binding of both ATP and D-Ala molecules. Solution affinity and kinetic studies showed that DCS interacts with Ddl in a manner similar to that observed for D-Ala. Each ligand binds to two binding sites that have significant differences in affinity, with the first binding site exhibiting high affinity. DCS inhibits the enzyme, with a 50% inhibitory concentration (IC(50)) of 0.37 mM under standard assay conditions, implicating a preferential and weak inhibition at the second, lower-affinity binding site. Moreover, DCS binding is tighter at higher ATP concentrations. The crystal structure illustrates potential drugable sites that may result in the development of more-effective Ddl inhibitors.

Factores de riesgo y protectores en el medio universitario costarricense: investigación cualitativa-grupos focales / Risk and protective factors in the Costa Rican university environment

El informe representa representa el esfuerzo pionero de un grupo de profesionales que se enfrentan por primera vez a coordinar e implementar un estudio interuniversitario e interdisciplinario, bajo la modalidad de investigación cualitativa, utilizando la metodología de "grupos focales". El estudio constituye una aproximación y un análisis "del sentir" y de las "expresiones" de un grupo de estudiantes universitarios (seleccionados bajo las características que demanda un grupo focal), que con sus respuestas y reflexiones, llegan a sistematizar y complementar los datos cuantitativos (encuestas y entrevistas) existentes sobre el tema en cuestion.