BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature

BACKGROUND: To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. The successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools. RESULTS: Our approach to abbreviation recognition (AR) is based on machine-learning, which exploits a novel set of rich features to learn rules from training data. Tested on the AB3P corpus, our system demonstrated a F-score of 89.90% with 95.86% precision at 84.64% recall, higher than the result achieved by the existing best AR performance system. We also annotated a new corpus of 1200 PubMed abstracts which was derived from BioCreative II gene normalization corpus. On our annotated corpus, our system achieved a F-score of 86.20% with 93.52% precision at 79.95% recall, which also outperforms all tested systems. CONCLUSION: By applying our system to extract all short form-long form pairs from all available PubMed abstracts, we have constructed BIOADI. Mining BIOADI reveals many interesting trends of bio-medical research. Besides, we also provide an off-line AR software in the download section on http://bioagent.iis.sinica.edu.tw/BIOADI/

Dynamic analysis of linear synchronous machines

Author name used in this publication: S. L. HoAuthor name used in this publication: S. Y. YangAuthor name used in this publication: K. W. E. ChengRefereed conference paper2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling

Noninvasive portable sensors are becoming popular in biomedical engineering practice due to its ease of use. This paper investigates the estimation of human oxygen uptake (VO2) of treadmill exercises by using multiple portable sensors (wireless heart rate sensor and triaxial accelerometers). For this purpose, a multivariable Hammerstein model identification method is developed. Well designed PRBS type of exercises protocols are employed to decouple the identification of linear dynamics with that of nonlinearities of Hammerstein systems. The support vector machine regression is applied to model the static nonlinearities. Multivariable ARX modelling approach is used for the identification of dynamic part of the Hammerstein systems. It is observed the obtained nonlinear multivariable model can achieve better estimations compared with single input single output models. The established multivariable model has also the potential to facilitate dynamic estimation of energy expenditure for outdoor exercises, which is the next research step of this study. © 2008 IEEE

A Hybrid Neural Network for Graph-Based Human Pose Estimation from 2D Images

© 2013 IEEE. This paper investigates the problem of human pose estimation (HPE) from single 2-dimensional (2D) still images using a convolutional neural network (CNN). The aim was to train the CNN to analyze a 2D input image of a person to determine the person's pose. The CNN output was given in the form of a tree-structured graph of interconnected nodes representing 2D image coordinates of the person's body joints. A new data-driven tree-based model for HPE was validated and compared to the traditional anatomy-based tree-based structures. The effect of the number of nodes in anatomy-based tree-based structures on the accuracy of HPE was examined. The tree-based techniques were compared with non-tree-based methods using a common HPE framework and a benchmark dataset. As a result of this investigation, a new hybrid two-stage approach to the HPE estimation was proposed. In the first stage, a non-tree-based network was used to generate approximate results that were then passed for further refinement to the second, tree-based stage. Experimental results showed that both of the proposed methods, the data-driven tree-based model (TD_26) and the hybrid model (H_26_2B) lead to very similar results, obtaining 1% higher HPE accuracy compared to the benchmark anatomy-based model (TA_26) and 3% higher accuracy compared to the non-tree-based benchmark (NT_26_A). The best overall HPE results were obtained using the anatomy-based benchmark with the number of nodes increased from 26 to 50, which also significantly increased the computational cost

Gamma-ray lines and neutrons from solar flares

The energy spectrum of accelerated protons and nuclei at the site of a limb flare was derived by a technique, using observations of the time dependent flux of high energy neutrons at the Earth. This energy spectrum is very similar to the energy spectra of 7 disk flares for which the accelerated particle spectra was previously derived using observations of 4 to 7 MeV to 2.223 MeV fluence ratios. The implied spectra for all of these flares are too steep to produce any significant amount of radiation from pi meson decay. It is suggested that the observed 10 MeV gamma rays from the flare are bremsstrahlung of relativistic electrons

Soft tagging of overlapping high confidence gene mention variants for cross-species full-text gene normalization

Abstract Background Previously, gene normalization (GN) systems are mostly focused on disambiguation using contextual information. An effective gene mention tagger is deemed unnecessary because the subsequent steps will filter out false positives and high recall is sufficient. However, unlike similar tasks in the past BioCreative challenges, the BioCreative III GN task is particularly challenging because it is not species-specific. Required to process full-length articles, an ineffective gene mention tagger may produce a huge number of ambiguous false positives that overwhelm subsequent filtering steps while still missing many true positives. Results We present our GN system participated in the BioCreative III GN task. Our system applies a typical 2-stage approach to GN but features a soft tagging gene mention tagger that generates a set of overlapping gene mention variants with a nearly perfect recall. The overlapping gene mention variants increase the chance of precise match in the dictionary and alleviate the need of disambiguation. Our GN system achieved a precision of 0.9 (F-score 0.63) on the BioCreative III GN test corpus with the silver annotation of 507 articles. Its TAP-k scores are competitive to the best results among all participants. Conclusions We show that despite the lack of clever disambiguation in our gene normalization system, effective soft tagging of gene mention variants can indeed contribute to performance in cross-species and full-text gene normalization.</p

Omega-3 polyunsaturated fatty acids status and cognitive function in young women

© 2019 The Author(s). Background: Research indicates that low omega-3 polyunsaturated fatty acid (n-3 PUFA) may be associated with decreased cognitive function. This study examined the association between n-3 PUFA status and cognitive function in young Australian women. Methods: This was a secondary outcome analysis of a cross-sectional study that recruited 300 healthy women (18-35 y) of normal weight (NW: BMI 18.5-24.9 kg/m2) or obese weight (OB: BMI ≥30.0 kg/m2). Participants completed a computer-based cognition testing battery (IntegNeuro™) evaluating the domains of impulsivity, attention, information processing, memory and executive function. The Omega-3 Index (O3I) was used to determine n-3 PUFA status (percentage of EPA (20:5n-3) plus DHA (22:6n3) in the red cell membrane) and the participants were divided into O3I tertile groups: T1 6.75%. Potential confounding factors of BMI, inflammatory status (C-reactive Protein), physical activity (total MET-min/wk), alpha1-acid glycoprotein, serum ferritin and hemoglobin, were assessed. Data reported as z-scores (mean ± SD), analyses via ANOVA and ANCOVA. Results: Two hundred ninety-nine women (26.9 ± 5.4 y) completed the study (O3I data, n = 288). The ANOVA showed no overall group differences but a significant group × cognition domain interaction (p < 0.01). Post hoc tests showed that participants in the low O3I tertile group scored significantly lower on attention than the middle group (p = 0.01; ES = 0.45 [0.15-0.74]), while the difference with the high group was borderline significant (p = 0.052; ES = 0.38 [0.09-0.68]). After confounder adjustments, the low group had lower attention scores than both the middle (p = 0.01) and high (p = 0.048) groups. These findings were supported by univariate analyses which found significant group differences for the attention domain only (p = 0.004). Conclusions: Cognitive function in the attention domain was lower in women with lower O3I, but still within normal range. This reduced but normal level of cognition potentially provides a lower baseline from which cognition would decline with age. Further investigation of individuals with low n-3 PUFA status is warranted

Clothes size prediction from dressed-human silhouettes

© 2017, Springer International Publishing AG. We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1, s and the average estimation error of body sizes is 0.824, cm, which can satisfy the tolerance for customers to shop clothes online

Emulation of scan paths in sequential circuit synthesis

Scan paths are generally added to a sequential circuit in a final design for testability step. We present an approach to incorporate the behavior of a scan path during circuit synthesis, thus avoiding to implement the scan path shift register as a separate structural entity. The shift transitions of the scan path are treated as a part of the system functionality. Depending on the minimization strategy for the system logic, either the delay or the area of the circuit can be reduced compared to a conventional scan path. which may be interpreted as a special case of realizing the combinational logic. The approach is also extended to partial scan paths. It is shown that the resulting structure is fully testable and test patterns can be efficiently produced by a combinational test generator. The advantages of the approach are illustrated with a collection of finite state machine examples

Natural killer cells attenuate cytomegalovirus-induced hearing loss in mice

<div><p>Congenital cytomegalovirus (CMV) infection is the most common non-hereditary cause of sensorineural hearing loss (SNHL) yet the mechanisms of hearing loss remain obscure. Natural Killer (NK) cells play a critical role in regulating murine CMV infection via NK cell recognition of the Ly49H cell surface receptor of the viral-encoded m157 ligand expressed at the infected cell surface. This Ly49H NK receptor/m157 ligand interaction has been found to mediate host resistance to CMV in the spleen, and lung, but is much less effective in the liver, so it is not known if this interaction is important in the context of SNHL. Using a murine model for CMV-induced labyrinthitis, we have demonstrated that the Ly49H/m157 interaction mediates host resistance in the temporal bone. BALB/c mice, which lack functional Ly49H, inoculated with mCMV at post-natal day 3 developed profound hearing loss and significant outer hair cell loss by 28 days of life. In contrast, C57BL/6 mice, competent for the Ly49H/m157 interaction, had minimal hearing loss and attenuated outer hair cell loss with the same mCMV dose. Administration of Ly49H blocking antibody or inoculation with a mCMV viral strain deleted for the m157 gene rendered the previously resistant C57BL/6 mouse strain susceptible to hearing loss to a similar extent as the BALB/c mouse strain indicating a direct role of the Ly49H/m157 interaction in mCMV-dependent hearing loss. Additionally, NK cell recruitment to sites of infection was evident in the temporal bone of inoculated susceptible mouse strains. These results demonstrate participation of NK cells in protection from CMV-induced labyrinthitis and SNHL in mice.</p></div