A single kernel-based approach to extract drug-drug interactions from biomedical literature.

When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually.

[Pollution characteristics of microbial aerosols generated from a municipal sewage treatment plant].

To characterize the pollution characteristics of microbial aerosols emitted from municipal sewage treatment plants, microbial aerosols were sampled with an Andersen 6-stage impactor at different treatment units of a Xi'an sewage treatment plant between June 2011 and July 2011. The plate-culture and colony-counting methods were employed to determine the concentrations, particle size distributions and median diameters of the airborne bacteria, fungi and actinomycetes. The results showed that the highest concentrations of bacteria (7 866 CFU x m(-3) +/- 960 CFU x m(-3)) and actinomycetes (2 139 CFU x m(-3) +/- 227 CFU x m(-3)) were found in the sludge-dewatering house while the highest fungi concentration (2156 CFU x m(-3) +/- 119 CFU x m(-3)) in the oxidation ditch.

Integrating various resources for gene name normalization.

The recognition and normalization of gene mentions in biomedical literature are crucial steps in biomedical text mining. We present a system for extracting gene names from biomedical literature and normalizing them to gene identifiers in databases. The system consists of four major components: gene name recognition, entity mapping, disambiguation and filtering.

Hash subgraph pairwise kernel for protein-protein interaction extraction.

Extracting protein-protein interaction (PPI) from biomedical literature is an important task in biomedical text mining (BioTM). In this paper, we propose a hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the novel kernel is to use the hierarchical hash labels to express the structural information of subgraphs in a linear time.

Enhanced photon absorption of single nanowire α-Si solar cells modulated by silver core.

Single nanowire solar cells are a promising candidate as nanoelectronic power sources. Metallic cores were integrated in single nanowire solar cells, and the influence of the silver core on the absorption efficiency and the short circuit current was studied in this work. A Full-wave Vectorial Finite Element Method approach was used to rigorously solve Maxwell's equations in two dimensions.

Effects of surfactant on bubble hydrodynamic behavior under flotation-related conditions in wastewater.

Bubble behavior is fundamental to the performance of froth flotation operations used in wastewater treatment processes. To fully understand and characterize bubble behavior under flotation-related conditions in wastewater, the high-speed photographic method has been employed to examine the motion of single bubbles and size distribution of bubble swarms with intermediate sizes ranging from 1 to 4 mm in the presence of surfactants in a laboratory scale flotation column. Both distilled water and synthetic municipal wastewater have been used to make solutions as well as two types of common surfactants.

Neurobiology and neuropathophysiology of obstructive sleep apnea.

Significant advancements have been made over the past three decades to better understand the disease entity of obstructive sleep apnea and the mechanisms by which this prevalent disorder imparts injury. Once considered a disorder of reversible sleepiness and insignificant arterial oxygen desaturations because of their intermittency, obstructive sleep apnea is now considered an independent risk factor for cardiovascular morbidity and mortality and an important contributor to neurocognitive impairment and neural injury as well as metabolic dysfunction. The rapidly fluctuating oxygen patterns are now believed to be central to oxidative injury in the brain and peripheral organs.

Enhancing biomedical text summarization using semantic relation extraction.

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g.

Neighborhood hash graph kernel for protein-protein interaction extraction.

Automated extraction of protein-protein interactions (PPIs) from biomedical literatures is an important topic of biomedical text mining. In this paper, we propose an approach based on neighborhood hash graph kernel for this task. In contrast to the existing graph kernel-based approaches for PPI extraction, the proposed approach not only has the capability to make use of full dependency graphs to represent the sentence structure but also effectively control the computational complexity.

Ranking support vector machine for multiple kernels output combination in protein-protein interaction extraction from biomedical literature.

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature.

Multiple kernel learning in protein-protein interaction extraction from biomedical literature.

Objective: Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. The volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database administrators, responsible for content input and maintenance to detect and manually update protein interaction information. The objective of this work is to develop an effective approach to automatic extraction of PPI information from biomedical literature.

Deletion of the Meq gene significantly decreases immunosuppression in chickens caused by pathogenic Marek's disease virus.

Background: Marek's disease virus (MDV) causes an acute lymphoproliferative disease in chickens, resulting in immunosuppression, which is considered to be an integral aspect of the pathogenesis of Marek's disease (MD). A recent study showed that deletion of the Meq gene resulted in loss of transformation of T-cells in chickens and a Meq-null virus, rMd5ΔMeq, could provide protection superior to CVI988/Rispens.

Results: In the present study, to investigate whether the Meq-null virus could be a safe vaccine candidate, we constructed a Meq deletion strain, GX0101ΔMeq, by deleting both copies of the Meq gene from a pathogenic MDV, GX0101 strain, which was isolated in China.

Deletion of 1.8-kb mRNA of Marek's disease virus decreases its replication ability but not oncogenicity.

Background: The 1.8-kb mRNA was reported as one of the oncogenesis-related genes of Marek's disease virus (MDV). In this study, the bacterial artificial chromosome (BAC) clone of a MDV field strain GX0101 was used as the platform to generate mutant MDV to examine the functional roles of 1.

Particle and naked RNA mycoviruses in industrially cultivated mushroom Pleurotus ostreatus in China.

Many cultivated mushroom strains, such as Pleurotus ostreatus TD300, displayed symptoms of degeneration. A spherical virus POSV and four dsRNA segments were extracted from mycelium of P. ostreatus TD300.

A framework for semisupervised feature generation and its applications in biomedical literature mining.

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.

[Evaluation the immuno-protective effect of an infectious clone of meq- deleted Marek's disease virus].

Objective: To evaluate the immuno-protective effect of GX0101 deltameq-BAC containing an infectious meq-null Marek's disease virus genome.

Method: One-day-old SPF birds were reared separately in isolators with positive filtered air. On day 1 of age, chickens immunized with 101 microg of GX0101A deltameq-BAC suspended in PBS, challenge infection with 500PFU very virulent rMD5 was performed at day 5 and 12 post-immunization separately.

CYP3AP1*3 allele is associated with lipid-lowering efficacy of simvastatin and atorvastatin in Chinese women.

Response to statin therapy for cardiovascular disease is variable among different individuals. The authors aimed to investigate the effect of the CYP3AP1*3 polymorphism on the lipid-lowering efficacy of statins. They recruited 379 unrelated hyperlipidemic patients: 202 (103 men) treated with simvastatin and 177 (87 men) with atorvastatin as single-agent therapy (20 mg day(-1) orally) for 4 weeks.

[Protective immunity of a meq-deleted Marek's disease virus against very virulent virus challenge in chickens].

Objective: To evaluate the pathogenicity and immunoprotective effect of a wild strain of Marek's Disease Virus (MDV) after deleting of its meq oncogene.

Methods: Total 150 one-day-old SPF chickens were divided into 5 groups of 30 birds each and kept in 5 isolators with positive pressure-filtered air. At first day, 2000 PFU of meq-deleted GX0101 deltameq were inoculated intra-abdominally (i.

Neuropeptide S mitigates spatial memory impairment induced by rapid eye movement sleep deprivation in rats.

Rapid eye movement (REM) sleep deprivation causes learning and memory deficits. Neuropeptide S, a newly discovered neuropeptide, has been shown to regulate arousal, anxiety, and may enhance long-term memory formation and spatial memory. However, it is unknown whether neuropeptide S could improve the REM sleep deprivation-induced memory impairment.

Learning an enriched representation from unlabeled data for protein-protein interaction extraction.

Background: Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used with great success in this task but they tend to suffer from data sparseness because of their restriction to obtain knowledge from limited amount of labelled data. In this work, we study the use of unlabeled biomedical texts to enhance the performance of supervised learning for this task.

BioPPISVMExtractor: a protein-protein interaction extractor for biomedical literature using SVM and rich feature sets.

Protein-protein interactions play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to detect and curate protein interaction information manually.

Incorporating rich background knowledge for gene named entity classification and recognition.

Background: Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part.

A review of methods of clinical image quality evaluation in mammography.

Purpose: Consistency in evaluation of mammography images in research and clinical practice is dependent on a standardised clinical image quality evaluation system. Currently two such systems are available-one developed by the American College of Radiology (ACR) and the other by the European Commission (EU guidelines). The purpose of this study was to review mammography clinical evaluation methods in research studies and their adherence to these systems.

Computational study on microscale behavior of bubble generated by aeration in a plug-flow aeration tank.

The microscale hydrodynamics of bubbles generated by aeration is directly related to the oxygen transfer efficiency and the overall performance of the activated sludge wastewater treatment process. To gain a deeper insight on the microscale phenomena of dispersed bubble occurring in this process, a three-dimensional direct simulation method is developed to study the effects of the liquid cross-flow on microscale behavior of bubble generation in a plug-flow aeration tank. The numerical simulations are performed using the level set method coupling with the governing equations of a single fluid with variable properties.

Effects of sleep restriction periods on serum cortisol levels in healthy men.

Objectives: To clarify effects of partial sleep deprivation (SD) on morning (07:00) serum cortisol concentrations in two protocols that restricted sleep to 3h/day in healthy adult men. The study was also designed to delineate the relationship between anxiety levels in the morning and slow wave sleep (SWS) periods at night.

Methods: Ten young adult Han Chinese males were recruited to participate in an 'earlier-night' sleep restriction (SR) period (sleep from 00:00 to 03:00) and then a 'later-night' SR period (sleep from 03:00 to 06:00).