Semi-automatic conversion of BioProp semantic annotation to PASBio annotation

<p>Abstract</p> <p>Background</p> <p>Semantic role labeling (SRL) is an important text analysis technique. In SRL, sentences are represented by one or more predicate-argument structures (PAS). Each PAS is composed of a predicate (verb) and several arguments (noun phrases, adverbial phrases, etc.) with different semantic roles, including main arguments (agent or patient) as well as adjunct arguments (time, manner, or location). PropBank is the most widely used PAS corpus and annotation format in the newswire domain. In the biomedical field, however, more detailed and restrictive PAS annotation formats such as PASBio are popular. Unfortunately, due to the lack of an annotated PASBio corpus, no publicly available machine-learning (ML) based SRL systems based on PASBio have been developed. In previous work, we constructed a biomedical corpus based on the PropBank standard called BioProp, on which we developed an ML-based SRL system, BIOSMILE. In this paper, we aim to build a system to convert BIOSMILE's BioProp annotation output to PASBio annotation. Our system consists of BIOSMILE in combination with a BioProp-PASBio rule-based converter, and an additional semi-automatic rule generator.</p> <p>Results</p> <p>Our first experiment evaluated our rule-based converter's performance independently from BIOSMILE performance. The converter achieved an F-score of 85.29%. The second experiment evaluated combined system (BIOSMILE + rule-based converter). The system achieved an F-score of 69.08% for PASBio's 29 verbs.</p> <p>Conclusion</p> <p>Our approach allows PAS conversion between BioProp and PASBio annotation using BIOSMILE alongside our newly developed semi-automatic rule generator and rule-based converter. Our system can match the performance of other state-of-the-art domain-specific ML-based SRL systems and can be easily customized for PASBio application development.</p

Hypercoagulability progresses to hypocoagulability during evolution of acetaminophen-induced acute liver injury in pigs

Increases in prothrombin time (PT) and international normalised ratio (INR) characterise acute liver injury (ALI) and failure (ALF), yet a wide heterogeneity in clotting abnormalities exists. This study defines evolution of coagulopathy in 10 pigs with acetaminophen (APAP)-induced ALI compared to 3 Controls. APAP administration began at 0 h and continued to ‘ALF’, defined as INR >3. In APAP pigs, INR was 1.05 ± 0.02 at 0 h, 2.15 ± 0.43 at 16 h and > 3 at 18 ± 1 h. At 12 h thromboelastography (TEG) demonstrated increased clot formation rate, associated with portal vein platelet aggregates and reductions in protein C, protein S, antithrombin and A Disintegrin and Metalloprotease with Thrombospondin type 1 repeats–13 (ADAMTS-13) to 60%, 24%, 47% and 32% normal respectively. At 18 ± 1 h, INR > 3 was associated with: hypocoagulable TEG profile with heparin-like effect; falls in thrombin generation, Factor V and Factor VIII to 52%, 19% and 17% normal respectively; further decline in anticoagulants; thrombocytopenia; neutrophilia and endotoxemia. Multivariate analysis, found that ADAMTS-13 was an independent predictor of a hypercoagulable TEG profile and platelet count, endotoxin, Protein C and fibrinogen were independent predictors of a hypocoagulable TEG profile. INR remained normal in Controls. Dynamic changes in coagulation occur with progression of ALI: a pro-thrombotic state progresses to hypocoagulability

Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach bridges the gap between fast, cooccurrence-based approaches lacking semantic relations and highly specialized and computationally demanding NLP approaches

Neurochemical Properties of the Synapses in the Pathways of Orofacial Nociceptive Reflexes

The brainstem premotor neurons of the facial nucleus (VII) and hypoglossal (XII) nucleus can integrate orofacial nociceptive input from the caudal spinal trigeminal nucleus (Vc) and coordinate orofacial nociceptive reflex (ONR) responses. However, the synaptoarchitectures of the ONR pathways are still unknown. In the current study, we examined the distribution of GABAergic premotor neurons in the brainstem local ONR pathways, their connections with the Vc projections joining the brainstem ONR pathways and the neurochemical properties of these connections. Retrograde tracer fluoro-gold (FG) was injected into the VII or XII, and anterograde tracer biotinylated dextran amine (BDA) was injected into the Vc. Immunofluorescence histochemical labeling for inhibitory/excitatory neurotransmitters combined with BDA/FG tracing showed that GABAergic premotor neurons were mainly distributed bilaterally in the ponto-medullary reticular formation with an ipsilateral dominance. Some GABAergic premotor neurons made close appositions to the BDA-labeled fibers coming from the Vc, and these appostions were mainly distributed in the parvicellular reticular formation (PCRt), dorsal medullary reticular formation (MdD), and supratrigeminal nucleus (Vsup). We further examined the synaptic relationships between the Vc projecting fibers and premotor neurons in the VII or XII under the confocal laser-scanning microscope and electron microscope, and found that the BDA-labeled axonal terminals that made asymmetric synapses on premotor neurons showed vesicular glutamate transporter 2 (VGluT2) like immunoreactivity. These results indicate that the GABAergic premotor neurons receive excitatory neurotransmission from the Vc and may contribute to modulating the generation of the tonic ONR

Synthesis and Enhanced Field-Emission of Thin-Walled, Open-Ended, and Well-Aligned N-Doped Carbon Nanotubes

Thin-walled, open-ended, and well-aligned N-doped carbon nanotubes (CNTs) on the quartz slides were synthesized by using acetonitrile as carbon sources. As-obtained products possess large thin-walled index (TWI, defined as the ratio of inner diameter and wall thickness of a CNT). The effect of temperature on the growth of CNTs using acetonitrile as the carbon source was also investigated. It is found that the diameter, the TWI of CNTs increase and the Fe encapsulation in CNTs decreases as the growth temperature rises in the range of 780–860°C. When the growth temperature is kept at 860°C, CNTs with TWI = 6.2 can be obtained. It was found that the filed-emission properties became better as CNT growth temperatures increased from 780 to 860°C. The lowest turn-on and threshold field was 0.27 and 0.49 V/μm, respectively. And the best field-enhancement factors reached 1.09 × 105, which is significantly improved about an order of magnitude compared with previous reports. In this study, about 30 × 50 mm2 free-standing film of thin-walled open-ended well-aligned N-doped carbon nanotubes was also prepared. The free-standing film can be transferred easily to other substrates, which would promote their applications in different fields

HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features

<p>Abstract</p> <p>Background</p> <p>The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers' primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction.</p> <p>Results</p> <p>We first compare the retrieval performance achieved by individually adding template features and position features to the baseline system. Then, the combination of both is examined. We found that using position features can almost double the original AUC score (0.8140vs.0.4936) of the baseline system. However, adding template features only results in marginal improvement (0.0197). Including both improves AUC to 0.8184, indicating that these two sets of features are complementary, and do not have overlapping effects. We then examine the performance in a different domain--diabetes, and the result shows a satisfactory AUC of 0.83.</p> <p>Conclusion</p> <p>Our approach successfully exploits template features to recognize true hypertension-related gene mentions and position features to distinguish key genes from other related genes. Templates are automatically generated and checked by biologists to minimize labor costs. Our approach integrates the advantages of machine learning models and pattern matching. To the best of our knowledge, this the first systematic study of extracting hypertension-related genes and the first attempt to create a hypertension-gene relation corpus based on the GAD database. Furthermore, our paper proposes and tests novel features for extracting key hypertension genes, such as relative position, section, and template features, which could also be applied to key-gene extraction for other diseases.</p

High-risk HPV E5-induced cell fusion: a critical initiating event in the early stage of HPV-associated cervical cancer

<p>Abstract</p> <p>Background</p> <p>Cervical cancer is strongly associated with high-risk human papillomavirus (HPV) and viral oncoproteins E5, E6 and E7 can transform cells by various mechanisms. It is proposed that oncogenic virus-induced cell fusion may contribute to oncogenesis if p53 or apoptosis is perturbed simultaneously. Recently, HPV-16 E5 was found to be necessary and sufficient for the formation of tetraploid cells, which are frequently found in precancerous cervical lesions and its formation is strongly associated with HPV state.</p> <p>Presentation of the hypothesis</p> <p>We propose that high-risk HPV E5-induced cell fusion is a critical initiating event in the early stage of HPV-associated cervical cancer.</p> <p>Testing the hypothesis</p> <p>Our hypothesis can be tested by comparing the likelihood for colony formation or tumorigenic ability in nude mice between normal HaCaT cells expressing all three oncogenic proteins and E5-induced bi-nucleated HaCaT cells expressing E6 and E7. Moreover, investigating premature chromosome condensation (PCC) in HPV-positive and negative precancerous cervical cells is another way to assess this hypothesis.</p> <p>Implication of the hypothesis</p> <p>This viewpoint would change our understanding of the mechanisms by which HPV induces cervical cancer. According to this hypothesis, blocking E5-induced cell fusion is a promising way to prevent the progression of cervical cancer. Additionally, establishment of a role of cell fusion in cervical carcinogenesis is of reference value for understanding the pathogenesis of other virus-associated cancers.</p

G protein-coupled receptor kinase 5 mediates Tazarotene-induced gene 1-induced growth suppression of human colon cancer cells

<p>Abstract</p> <p>Background</p> <p>Tazarotene-induced gene 1 (TIG1) is a retinoid-inducible type II tumour suppressor gene. The B isoform of TIG1 (TIG1B) inhibits growth and invasion of cancer cells. Expression of TIG1B is frequently downregulated in various cancer tissues; however, the expression and activities of the TIG1A isoform are yet to be reported. Therefore, this study investigated the effects of the TIG1A and TIG1B isoforms on cell growth and gene expression profiles using colon cancer cells.</p> <p>Methods</p> <p>TIG1A and TIG1B stable clones derived from HCT116 and SW620 colon cancer cells were established using the GeneSwitch system; TIG1 isoform expression was induced by mifepristone treatment. Cell growth was assessed using the WST-1 cell proliferation and colony formation assays. RNA interference was used to examine the TIG1 mediating changes in cell growth. Gene expression profiles were determined using microarray and validated using real-time polymerase chain reaction, and Western blot analyses.</p> <p>Results</p> <p>Both TIG1 isoforms were expressed at high levels in normal prostate and colon tissues and were downregulated in colon cancer cell lines. Both TIG1 isoforms significantly inhibited the growth of transiently transfected HCT116 cells and stably expressing TIG1A and TIG1B HCT116 and SW620 cells. Expression of 129 and 55 genes was altered upon induction of TIG1A and TIG1B expression, respectively, in stably expressing HCT116 cells. Of the genes analysed, 23 and 6 genes were upregulated and downregulated, respectively, in both TIG1A and TIG1B expressing cells. Upregulation of the G-protein-coupled receptor kinase 5 (GRK5) was confirmed using real-time polymerase chain reaction and Western blot analyses in both TIG1 stable cell lines. Silencing of TIG1A or GRK5 expression significantly decreased TIG1A-mediated cell growth suppression.</p> <p>Conclusions</p> <p>Expression of both TIG1 isoforms was observed in normal prostate and colon tissues and was downregulated in colon cancer cell lines. Both TIG1 isoforms suppressed cell growth and stimulated GRK5 expression in HCT116 and SW620 cells. Knockdown of GRK5 expression alleviated TIG1A-induced growth suppression of HCT116 cells, suggesting that GRK5 mediates cell growth suppression by TIG1A. Thus, TIG1 may participate in the downregulation of G-protein coupled signaling by upregulating GRK5 expression.</p