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Purpose: Researchers have identified gut microbiota that interact with brain regions associated with emotion and mood. Literature reviews of those associations rely on rigorous systematic approaches and labor-intensive investments. Here we explore how knowledge graph, a large scale semantic network consisting of entities and concepts as well as the semantic relationships among them, is incorporated into the emotion-probiotic relationship exploration work. Method: We propose an end-to-end emotion-probiotics relationship exploration method with an integrated medical knowledge graph, which incorporates the text mining output of knowledge graph, concept reasoning and evidence classification. Specifically, a knowledge graph for probiotics is built based on a text-mining analysis of PubMed, and further used to retrieve triples of relationships with reasoning logistics. Then specific relationships are annotated and evidence levels are retrieved to form a new evidence-based emotion-probiotic knowledge graph. Results: Based on the probiotics knowledge graph with 40,442,404 triples, totally 1453 PubMed articles were annotated in both the title level and abstract level, and the evidence levels were incorporated to the visualization of the explored emotion-probiotic relationships. Finally, we got 4131 evidenced emotion-probiotic associations. Conclusions: The evidence-based emotion-probiotic knowledge graph construction work demonstrates an effective reasoning based pipeline of relationship exploration. The annotated relationship associations are supposed be used to help researchers generate scientific hypotheses or create their own semantic graphs for their research interests.

Swift, non-destructive detection approaches should address the problem of insufficient sensitivity when attempting to obtain and perceive live crab information in low-light environments caused by the crab’s phototaxis. We propose a learning-based low-illumination image enhancer (LigED) for effective enhanced lighting and elimination of darkness in images. The camera response function was combined with the reflectance ground-truth mechanism of image decomposition. Self-attention units were then introduced in the reflectance restoration network to adjust the illumination to avoid visual defects, thus jointly strengthening the adaptability of dark-light enhancement and ability to perceive crab information. Convolutional neural network (CNN)-based detection methods can further enhance the algorithm’s robustness to light and adaptability to different environments, which motivated the development of a scalable lightweight live crab detector (EfficientNet-Det0) utilizing the two-stage compound scaling CNN approach. The lightness order error and natural image quality evaluator based on the proposed methods were 251.26 and 11.60, respectively. The quality of average precision detection increased by 13.84–95.40%. The fastest detection speed of a single image was 91.74/28.41 f·s−1 using a common GPU/CPU, requiring only 15.1 MB of storage, which advocates for the utilization of LigED and EfficientNet-Det0 for the efficient detection of underwater live crabs.

Background To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning based tools. To support the chemical disease semantic relation extraction especially for chronic diseases, a chronic disease specific corpus for combination therapy discovery in Chinese (RCorp) is manually annotated. Methods In this study, we extracted abstracts from a Chinese medical literature server and followed the annotation framework of the BioCreative CDR corpus, with the guidelines modified to make the combination therapy related relations available. An annotation tool was incorporated to the standard annotation process. Results The resulting RCorp consists of 339 Chinese biomedical articles with 2367 annotated chemicals, 2113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. Each annotation includes both the mention text spans and normalized concept identifiers. The corpus gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. Conclusions We extracted and manually annotated a chronic disease specific corpus for combination therapy discovery in Chinese. The result analysis of the corpus proves its quality for the combination therapy related knowledge discovery task. Our annotated corpus would be a useful resource for the modelling of entity recognition and relation extraction tools. In the future, an evaluation based on the corpus will be held.

Itraconazole, an effective broad-spectrum antifungal drug, has been well established for its anticancer activity in cancers including melanoma. However, details concerning its underlying mechanism in melanoma are unclear. This work investigated the function of itraconazole-induced 5'-monophosphate (AMP)-activated protein kinase alpha (AMPKα) in melanoma progression through ERK signaling. The AMPKα level in melanoma tissues and cells was assessed by RT-qPCR and western blot. Survival analysis of patients with melanoma based on the AMPKα expression level was performed according to TCGA database. Melanoma cell proliferation, migration, and invasion were examined using CCK-8, colony formation, wound healing, and Transwell assays. A xenograft tumor model was established to examine the effect of itraconazole on tumor growth in vivo. The AMPKα mRNA and protein levels were reduced in melanoma tissues and cells. A low expression of AMPKα indicated a poor prognosis. Functionally, itraconazole restrained melanoma cell proliferation, migration, and invasion by upregulating AMPKα. Itraconazole activated AMPK signaling and inhibited ERK signaling in melanoma cells. Activation of ERK signaling reversed the effect of itraconazole on cellular process in melanoma. Moreover, itraconazole-induced AMPKα inhibited melanoma tumor growth in vivo by inhibiting ERK signaling. Itraconazole-induced AMPKα inhibits the progression of melanoma by inhibition of ERK signaling.

The skins of most mature apple fruits are incompletely red and also include green and pale yellow color, which increases the difficulty of fruit detection by machine vision. A detection method based on color and shape features is proposed for this kind of apple fruits. Simple linear iterative clustering (SLIC) is adapted to segment images taken in orchards into super-pixel blocks. The color feature extracted from blocks is used to determine candidate regions, which can filter a large proportion of non-fruit blocks and improve detection precision. Next, the histogram of oriented gradient (HOG) is adopted to describe the shape of fruits, which is applied to detect fruits in candidate regions and locate the position of fruits further. The proposed method was tested by images taken under different illuminations. The average values of recall, precision, and F1 reach 89.80%, 95.12%, and 92.38% respectively. The performance of detecting fruits covered at different levels is also tested. The values of the recall are all more than 85%, which indicates that proposed method can detect a great part of covered fruits. Compared with pedestrian detection method and faster region-based convolutional neural network (RCNN), the proposed method has the best performance and higher than faster RCNN slightly. However, the proposed method is not robust to noise and its elapsed time of one image is 1.94 s and less than faster RCNN.

BACKGROUND Medical postgraduates’ demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important. OBJECTIVE This study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school. METHODS We developed a medical data mining course called “Practical Techniques of Medical Data Mining” for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms—Rain Classroom, Tencent Meeting, and WeChat—were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience. RESULTS In total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students’ characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated “very positive” or “positive”), and MedHub received the best feedback, both in function (80/83, 96% chose “satisfied”) and teaching effect (80/83, 96% chose “satisfied”). The case study showed that our course was able to fill the gap between student expectations and learning outcomes. CONCLUSIONS We developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.

Feeding is an important and also a heavy work in river crab farming, uniform feeding is necessary for raising river crab on the whole pond. The overall scheme of automatic bait casting system carried by paddlewheel catamaran is proposed based on RTK-GPS navigation positioning device. The feeding device equipped with the controllable flow velocity of bait feeder, distance adjustable thrower and bait weight measurable device which could perform variable feeding is designed to solve the problem of nonuniform distribution of the feed. The hardware and software system of automatic feeding device are designed. Then the calculation and simulation for the modeling of the feeding device are pefromed. The test results show that the system linear track positioning accuracy is less than 30 cm, and automatic continuous feeding speed is up to 240 m2/min. The results of this study provide technology reference for further study of automatic uniform feeding on the whole pond in river crab aquaculture.

Development of the novel photocatalyst with outstanding photocatalytic activity is of huge interest for photocatalytic degradation of organic pollution using solar energy. In this work, a new photocatalyst was achieved by CdS nanoparticles decorated K+Ca2Nb3O10− (KCNO) nanosheets. The as-fabricated CdS/KCNO hybrids exhibited well-defined morphology and enhanced photocatalytic activity than pristine KCNO. On the basis of the physicochemical characterization and photocatalytic performance test, it is believed that the CdS nanoparticles can effectively promote the photo-induced charge separation of KCNO nanosheets, and the relative mechanism has been investigated.

In order to address the issues of nonuniform feeding and high labor cost plaguing the process of farming Chinese river crabs, the present study proposes a multifunctional automatic river crabs feeding boat based on Advanced RISC Machine (ARM) and Global Positioning System / Inertial Navigation System (GPS/INS) integrated navigation. This paper proposes a new calculation method based on real time point insertion. This method calculates the current target position of the boat in the real time according to the position of the boat and the turning points of the current route. A new turning and route-switching strategy is also presented in this paper to improve the ship's operational efficiency and prevent the ship from veering off the target route due to its high speed. Considering the boat's unique movement characteristics including non-linearity, large delay and underdamped nature, a route-speed dual-loop control algorithm is designed based on fuzzy Proportion Integration Differentiation (PID) method. Through analyzing the bait distribution associated with the feeding machine, the present study proposes an inner-spiral-based full coverage traversal method and a travel distance optimization model so as to improve the uniformity of the automatic feeding. Results show that the speed overshoot is no more than 5% and the steady-state error can be kept within 3%. Compared with the finite point method, the real time point insertion method decreases the peak route deviation errors by 82.82% and 84.14% while turning and going straight.

BackgroundMedical postgraduates’ demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important.ObjectiveThis study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school.MethodsWe developed a medical data mining course called “Practical Techniques of Medical Data Mining” for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms—Rain Classroom, Tencent Meeting, and WeChat—were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience.ResultsIn total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students’ characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated “very positive” or “positive”), and MedHub received the best feedback, both in function (80/83, 96% chose “satisfied”) and teaching effect (80/83, 96% chose “satisfied”). The case study showed that our course was able to fill the gap between student expectations and learning outcomes.ConclusionsWe developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.

This paper describes a robust sparse Bayesian learning approach for DOA estimation in the presence of impulsive noise. In practical situations, impulses in the noise often appear in bursts, resulting in structured rather than independent sparsity, which can be exploited to enhance DOA estimation performance. However, there is no method available for capturing the realistic burst structure of impulsive noise in the literature. In this paper, we model the structured impulsive noise with a novel two-dimensional burst prior and develop a robust variational Bayesian inference (VBI) framework for DOA estimation under burst impulsive noise, and an improved grid refinement method is further proposed to adjust grid points of the dictionary matrix, which can combat the off-grid gap more efficiently. The superior impulse-resistant DOA estimation performance and computational efficiency of our proposed method are verified by simulation results.

This paper proposes a robust model predictive control (RMPC) approach for the automatic operation boats to cast baits evenly along desired paths. The difficulties in the control design come from the control system model, which is nonlinear, underactuated, input saturated, and disturbed by time-varying signals. The RMPC overcomes these difficulties by the receding horizon optimization explicitly considering the input saturation and using the mixed H 2 / H ∞ cost function. To decrease computational complexity of the RMPC, a polyhedral model is constructed as the predictive model based on dynamics of the path-following error. The feasibility and effectiveness of the proposed path-following control is verified by theoretical analysis and illustrated by simulations and experiments.

// Xuexiong Song 1, * , Fangzheng Li 1, * , Zhongling Jiang 1, * , Yueping Sun 1 , Huatao Li 1 , Shansong Gao 1 , Liping Zhang 1 , Binghua Xue 2 , Guimin Zhao 3 , Jingyu Li 2 , Zhonghua Liu 2 , Hongbin He 3 and Yanjun Huan 1 1 College of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong Province, China 2 College of Life Science, Northeast Agricultural University, Harbin, Heilongjiang Province, China 3 College of Life Science, Shandong Normal University, Jinan, Shandong Province, China * These authors contributed equally to this work Correspondence to: Yanjun Huan, email: huanyanjun1982@163.com Keywords: imprinting, donor cell, somatic cell nuclear transfer, cloned embryo, pig Received: June 20, 2017 Accepted: August 06, 2017 Published: August 22, 2017 ABSTRACT Imprinting disorder during somatic cell nuclear transfer usually leads to the abnormality of cloned animals and low cloning efficiency. However, little is known about the role of donor cell imprinting in the development of cloned embryos. Here, we demonstrated that the imprinting (H19/Igf2) in porcine fetus fibroblasts derived from the morphologically abnormal cloned fetuses (the abnormal imprinting group) was more hypomethylated, and accordingly, significantly higher H19 transcription and lower Igf2 expression occurred in comparison with those in fibroblasts derived from morphologically normal cloned fetuses (the normal imprinting group) or donor fetus fibroblasts (the control group). When these fibroblasts were used as donor cells, the abnormal imprinting group displayed an even lower imprinting methylation level, in correspondence to the significantly downregulated expression of Dnmt1, Dnmt3a and Zfp57, and a markedly reduced blastocyst rate, while the normal imprinting group took on the similar patterns of imprinting, gene expression and embryo development to the control group. When 5-aza-dC was applied to reduce the fibroblasts imprinting methylation level in the normal imprinting group, cloned embryos displayed the more severely impaired imprinting and significantly lower blastocyst rate. While the upregulated H19 transcription in the abnormal imprinting group was knocked down, the imprinting statuses were partly rescued, and the cleavage and blastocyst rates significantly increased in cloned embryos. In all, donor cell imprinting disorder reduced the developmental efficiency of cloned embryos. This work provides a new insight into understanding the molecular mechanism of donor cells regulating the cloned embryo development.

We systematically developed a prognostic model for pancreatic cancer that was compatible across different transcriptomic platforms and patient cohorts. After performing quality control measures, we used seven microarray datasets and two RNA sequencing datasets to identify consistently dysregulated genes in pancreatic cancer patients. Weighted gene co-expression network analysis was performed to explore the associations between gene expression patterns and clinical features. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct a prognostic model. We tested the predictive power of the model by determining the area under the curve of the risk score for time-dependent survival. Most of the differentially expressed genes in pancreatic cancer were enriched in functions pertaining to the tumor immune microenvironment. The transcriptome profiles were found to be associated with overall survival, and four genes were identified as independent prognostic factors. A prognostic risk score was then proposed, which displayed moderate accuracy in the training and self-validation cohorts. Furthermore, patients in two independent microarray cohorts were successfully stratified into high- and low-risk prognostic groups. Thus, we constructed a reliable prognostic model for pancreatic cancer, which should be beneficial for clinical therapeutic decision-making.

Linear orbit tracking algorithm of navigation for aquaculture paddle boat. Based on the motion coordinate system, after the establishment of the paddle boat hydrodynamic model, the transfer function and state equation of the paddle boat yaw angle system, the sliding mode control method is adopted to achieving high-precision track tracking for the paddle boat. In order to save the energy, reduce the active frequency of the control system caused by the disturbance, and extend the duration of sailing of the paddle boat, the Kalman filter is proposed to reduce the influence of the disturbance on the track tracking. The simulation and experimental verification show that the sliding mode control method based on Kalman filter can realize high-precision linear track tracking, and Kalman filter can effectively reduce the influence of disturbance.

To identify knowledge of combination therapy for chronic diseases, high-quality manually curated corpora are required. In this study, we describe the construction work of a corpus for chemical disease semantic extraction in Chinese (RCorp) from a collection of Chinese biomedical abstracts. During the RCorp corpus construction, we used domain-existing guidelines as references, and incorporated annotation tools to the standard annotation process. The resulting corpus consists of 339 Chinese biomedical articles with 2,367 annotated chemicals, 2,113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. RCorp gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. The result analysis of the corpus proves the quality of the corpus for the combination therapy of chronic diseases knowledge discovery task.

Effective biomass detection methods utilizing computer vision techniques should be capable of handling large differences in object posture and various degrees of mutual overlap/occlusion in object scenes. We propose an automatic coarse-to-fine joint detection and instance segmentation network (JDSNet) that can perform real-time detection and instance segmentation on underwater non-structural live crabs in real-time. The method was adapted to non-structural objects and mobile devices by applying the anchor-free mechanism and center-ness strategy of the fully convolutional one-stage prediction head and the improved energy-efficient backbone IVoVNet-19-DW with identity mapping and channel attention. This approach avoided the complicated computations related to the anchor-based mechanism and effectively generated the features of various receiving domains, jointly improving the memory access speed and accuracy of predicting the bounding boxes of different instances. A novel spatial attention-guided mask branch was then added to focus on irregular occluded object pixels and conduct precise pixel-level mask segmentation within the predicted coarse instance-aware rectangular-bounding boxes. The experimental analysis using the proposed method resulted in a quality of detection F1 and segmentation Dic of 97.7% and 94.6%, respectively. The fastest detection speed of a single image was 48.07/13.32 fps (~10 times faster than the existing network Mask RCNN) on a commonly configured GPU/CPU, requiring only 7.04 MB of storage (~25 times smaller than Mask RCNN). It indicates that JDSNet can segment various non-structural live crabs and perform biomass statistics robustly and efficiently, exhibiting significance for precision feeding applications in automatic feeding boats.

The bait feeding of river crab farming needs to be covered evenly on the whole pond. At present, feeding is mainly carried out by manual driving or remote control ship-borne feeding machine on the pond. The feeding accuracy and efficiency are relatively low, and it is difficult to guarantee feeding effect. In view of the above situation, based on the mobile automatic feeding boat equipped with GPS, this paper proposes a complete coverage trajectory planning method with irregular quadrilateral pond, and a trajectory planning system based on SuperMap Objects is designed to generate reciprocating traverse feeding trajectory automatically. The simulation is performed to verify the feasibility of the trajectory planning method. The results show that the trajectory planning system can meet the requirements of automatic and uniform feeding on the crab pond.

Image analysis technology has drawn dramatic attention and developed rapidly because it enables a non-extractive and non-destructive approach to data acquisition of crab aquaculture. Owing to the irregular shape, multi-scale posture and special underwater environment, it is very challenging to adopt the traditional image recognition methods to detect crabs quickly and effectively. Consequently, we propose a real-time and robust object detector, Faster MSSDLite, for detecting underwater live crabs. Lightweight MobileNetV2 is selected as the backbone of a single shot multi-box detector (SSD), and standard convolution is replaced by depthwise separable convolution in the prediction layers. A feature pyramid network (FPN) is adopted at low extra cost to improve the detection precision of multi-scale crabs and make up for the deficiency of SSD to force different network layers to learn the same features. More significantly, the unified quantized convolutional neural network (Quantized-CNN) framework is applied to quantify the error correction of the improved detector for further accelerating the computation of convolutional layers and compressing the parameters of fully-connected layers. The test results show that Faster MSSDLite has better performance than traditional SSD. The average precision (AP) and F1 score of detection are 99.01% and 98.94%, respectively. The detection speed can reach 74.07 frames per second in commonly configured microcomputers (~8× faster than SSD). The computation amount of floating-point numbers required by the detection is reduced to only 0.32 billion (~49× smaller than SSD), and the size of the model is compressed into 4.84 MB (~28× smaller than SSD). The model is also more robust, which can stably detect underwater live crabs in real-time, estimate the live crab biomass in water bodies automatically, and provide reliable feedback information for the fine feeding of automatic feeding boats.

In order to improve the performance of the closed-loop Buck converter control system, a compound control scheme based on nonlinear disturbance observer (DO) and nonsingular terminal sliding mode (TSM) was developed to control the Buck converter. The control design includes two steps. First of all, without considering the dynamic and steady-state performances, a baseline terminal sliding mode controller was designed based on the average model of the Buck converter, such that the desired value of output voltage could be tracked. Secondly, a nonlinear DO was designed, which yields an estimated value as the feedforward term to compensate the lumped disturbance. The compound controller was composed of the terminal sliding mode controller as the state feedback and the estimated value as the feedforward term. Simulation analysis and experimental verifications showed that compared with the traditional proportional integral derivative (PID) and terminal sliding mode state feedback control, the proposed compound control method can provide faster convergence performance and higher voltage output quality for the closed-loop system of the Buck converter.

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the meanR of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network- particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the meanT of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat.

Chemical-induced disease relations (CID) are crucial in various biomedical tasks. In the CID task of Biocreative V, no classifiers with multiple kernels have been developed. In this study, a multiple kernel learning-boosting (MKLB) method is proposed. Different kernel functions according to different types of features were constucted and boosted, each of which were learned with multiple kernels. Our multiple kernel learning-boosting (MKLB) method achieved a F1 score of 0.5068 without incorporating knowledge bases.

Different groups of audiences in health care: health professionals and health consumers, each have different information needs. Health monographs targeting different audiences are created by leveraging readers' background knowledge. The NCI's Physician Data Query (PDQ®) Cancer Information Summaries provide parallel cancer information and education resources with different target audiences. In this paper, we used targeted audience-specific cancer information PDQs to measure characteristic differences on the element level between audiences. In addition, we compared vocabulary coverage. Results show a significant difference between the professional and patient version of cancer monographs in both content organization and vocabulary. This study provides a new view to assess targeted audience-specific health information, and helps editors to improve the quality and readability of health information.

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. Given the nature of both tasks, a test collection is required to contain both disease/chemical annotations and relation annotations in the same set of articles. Despite previous efforts in biomedical corpus construction, none was found to be sufficient for the task. Thus, we developed our own corpus called BC5CDR during the challenge by inviting a team of Medical Subject Headings (MeSH) indexers for disease/chemical entity annotation and Comparative Toxicogenomics Database (CTD) curators for CID relation annotation. To ensure high annotation quality and productivity, detailed annotation guidelines and automatic annotation tools were provided. The resulting BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions. Each entity annotation includes both the mention text spans and normalized concept identifiers, using MeSH as the controlled vocabulary. To ensure accuracy, the entities were first captured independently by two annotators followed by a consensus annotation: The average inter-annotator agreement (IAA) scores were 87.49% and 96.05% for the disease and chemicals, respectively, in the test set according to the Jaccard similarity coefficient. Our corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/