Method for 3D modelling based on structure from motion processing of sparse 2D images

A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parameters

OAE: The Ontology of Adverse Events

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data

OAE: The Ontology of Adverse Events

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data

Identification of protein-protein interactions of human HtrA1.

The human heat shock protein HtrA1, a member of the HtrA family of serine proteases, is a evolutionarily highly conserved factor which displays a widespread pattern of expression. The yeast two-hybrid technique was employed to identify new cellular proteins physically interacting with HtrA1, and thus potential targets of this serine protease. An enzymatically inactive HtrA1 point mutant, HtrA1-S328A, was generated and used as bait in a yeast two-hybrid system. Fifty-two plasmids were isolated from primary positive yeast clones. Subsequent sequencing and BLAST analysis revealed cDNAs encoding for 13 different proteins. These putative binding partners of HtrA1 appeared to be a) components of extracellular matrix; b) factors related to signal pathways, and c) unknown proteins. Among the 13 positive clones identified and reported here, it is worth of note that the interaction of HtrA1 with tubulin and collagen (extracellular matrix proteins) and with tuberin (cytoplasmic protein) is confirmed by other studies, and this further supports previous findings in which HtrA1 can be found active as an intracytoplasmic protein or as secreted protein as well

BioCreative III interactive task: an overview

The BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining teams in developing basic capabilities relevant to biological curation, but they did not address the issues of system usage, insertion into the workflow and adoption by curators. Thus in BioCreative III (BC-III), the InterActive Task (IAT) was introduced to address the utility and usability of text mining tools for real-life biocuration tasks. To support the aims of the IAT in BC-III, involvement of both developers and end users was solicited, and the development of a user interface to address the tasks interactively was requested

A matrix decomposition perspective on calibrated photometric stereo

Leveraging on recent advances in robust matrix decomposition, we revisit Lambertian photometric stereo as a robust low-rank matrix recovery problem with both missing and corrupted entries, tailoring Grasta and R-GoDec to normal surface estimation. A method to automatically detect shadows is proposed. The performance of different robust matrix completion techniques are analyzed on the challenging DiLiGenT datasets