Biomedical subjects

Fernando Pereira

Publications and source records attributed to Fernando Pereira.

6 recordsLinked to original sources

PubMed2026

Stability in the face of global decline: a 20-year study of arthropods in an oceanic archipelago.

Insect declines are of global concern, yet no long-term ecological studies (LTER) have confirmed this trend on islands. This study utilises the first available LTER data on island arthropods, targeting epigeal and canopy species from the Azores Archipelago (Portugal), and covering over 20 years in three distinct sampling events from 30 standard sites. We investigate changes in abundance, biomass, and species richness within native forest arthropod communities, focusing on the proportions of endemic and introduced species, and temporal patterns among single-island endemics and forest-dependent endemics. Results reveal significant temporal variability, but overall abundance, biomass, and species richness remain stable across endemic and native non-endemic taxa. Among the species studied, 28% declined, 17% increased, and 55% showed no significant differences. Exotic invasions and related extinctions appear minimal. Forest-dependent endemic species declined below anticipated levels, suggesting that the extinction debt for these species may be less severe than initially expected. Nonetheless, some forest specialists have declined significantly, and seven species, not seen over 20 years, are considered to be extinct. The three-decade-long conservation of Azorean native forests may have contributed to the stability of some populations, thus these findings underscore the need for continued and enhanced conservation efforts of insular forest-associated diversity.

Animals↗

PubMed2005

Automatically annotating documents with normalized gene lists.

BACKGROUND: Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information extraction and database curation systems. Here we present two separate solutions to this problem. The first is primarily based on standard pattern matching and information extraction techniques. The second and more novel solution uses a statistical classifier to recognize valid gene matches from a list of known gene synonyms. RESULTS: We compare the results of the two systems, analyze their merits and argue that the classification based system is preferable for many reasons including performance, simplicity and robustness. Our best systems attain a balanced precision and recall in the range of 74%-92%, depending on the organism.

Animals↗

PubMed2005

Identifying gene and protein mentions in text using conditional random fields.

BACKGROUND: We present a model for tagging gene and protein mentions from text using the probabilistic sequence tagging framework of conditional random fields (CRFs). Conditional random fields model the probability P(t/o) of a tag sequence given an observation sequence directly, and have previously been employed successfully for other tagging tasks. The mechanics of CRFs and their relationship to maximum entropy are discussed in detail. RESULTS: We employ a diverse feature set containing standard orthographic features combined with expert features in the form of gene and biological term lexicons to achieve a precision of 86.4% and recall of 78.7%. An analysis of the contribution of the various features of the model is provided.

Genes↗

PubMed2004

An entity tagger for recognizing acquired genomic variations in cancer literature.

VTag is an application for identifying the type, genomic location and genomic state-change of acquired genomic aberrations described in text. The application uses a machine learning technique called conditional random fields. VTag was tested with 345 training and 200 evaluation documents pertaining to cancer genetics. Our experiments resulted in 0.8541 precision, 0.7870 recall and 0.8192 F-measure on the evaluation set.

Abstracting and Indexing↗

PubMed2004

Spatial shape error concealment for object-based image and video coding.

In this paper, an original spatial shape error-concealment technique, to be used in the context of object-based image and video coding schemes, is proposed. In this technique, it is assumed that the shape of the corrupted object at hand is in the form of a binary alpha plane, in which some of the shape data is missing due to channel errors. From this alpha plane, a contour corresponding to the border of the object can be extracted. However, due to errors, some parts of the contour will be missing and, therefore, the contour will be broken. The proposed technique relies on the interpolation of the missing contours with Bézier curves, which is done based on the available surrounding contours. After all the missing parts of the contour have been interpolated, the concealed alpha plane can be easily reconstructed from the fully recovered contour and used instead of the erroneous one improving the final subjective impact.

Algorithms↗

PubMed2004

Adaptive shape and texture intra refreshment schemes for improved error resilience in object-based video coding.

Video encoders may use several techniques to improve error resilience. In particular, for video encoders that rely on predictive (inter) coding to remove temporal redundancy, intra coding refreshment is especially useful to stop temporal error propagation when errors occur in the transmission or storage of the coded streams, since these errors may cause the decoded quality to decay very rapidly. In the context of object-based video coding, intra coding refreshment can be applied to both the shape and texture data. In this paper, novel shape and texture intra refreshment schemes are proposed which can be used by object-based video encoders, such as MPEG-4 video encoders, independently or combined. These schemes allow to adaptively determine when the shape and texture of the various video objects in a scene should be refreshed in order to maximize the decoded video quality for a certain total bit rate.

Algorithms↗