Volume 15 Supplement 12

Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013): Bioinformatics

Open Access

Enhancing the accuracy of knowledge discovery: a supervised learning method

  • Liangxi Cheng1,
  • Hongfei Lin1Email author,
  • Feng Zhou1,
  • Zhihao Yang1 and
  • Jian Wang1
BMC Bioinformatics201415(Suppl 12):S9

https://doi.org/10.1186/1471-2105-15-S12-S9

Published: 6 November 2014

Abstract

Background

The amount of biomedical literature available is growing at an explosive speed, but a large amount of useful information remains undiscovered in it. Researchers can make informed biomedical hypotheses through mining this literature. Unfortunately, popular mining methods based on co-occurrence produce too many target concepts, leading to the declining relevance ranking of the potential target concepts.

Methods

This paper presents a new method for selecting linking concepts which exploits statistical and textual features to represent each linking concept, and then classifies them as relevant or irrelevant to the starting concepts. Relevant linking concepts are then used to discover target concepts.

Results

Through an evaluation it is observed textual features improve the results obtained with only statistical features. We successfully replicate Swanson's two classic discoveries and find the rankings of potentially relevant target concepts are relatively high.

Conclusions

The number of target concepts is greatly reduced and potentially relevant target concepts gain higher ranking by adopting only relevant linking concepts. Thus, the proposed method has the potential to help biomedical experts find the most useful and valuable target concepts effectively.

Background

The amount of biomedical literature available is growing at an explosive speed. For example, MEDLINE, a bibliographic database in the field of biomedicine, contains over 16 million references to biomedical journal articles from approximately 5200 biomedical journals worldwide, with more than 2000 completed references added to it each day [1]. Given that people's ability to read substantial amounts of literature is limited and field biomedical experts generally concentrate on relatively narrow topics, the sheer amount of literature available may be a severe challenge for extracting complementary knowledge necessary to the practice of the relevant fields. In this regard, development of a computer-assisted, literature-based approach for mining hidden but central knowledge across disciplines to obtain previously undiscovered public knowledge is of utmost importance.

Swanson [2] initiated literature-based discovery (LBD) research in which two complementary samples of literature are considered: C literature (CL), which describes the C concepts, and A literature (AL), which describes the A concepts (the C and A concepts do not co-occur in any of these two paired literature samples). Meanwhile, B concepts, through which we form the hypothesis that the C and A concepts may have a hidden relation, are described in both CL and AL. Here, we treat CL and AL as non-interactive complementary literature samples and B concepts as linking concepts. Swanson found three linking concepts that led to the discovery of a hidden relation between Raynaud Syndrome and Fish Oil. Swanson's hypothesis was verified by DiGiacomo et al. [3] in 1989.

One problem in Swanson's method is that it requires a large amount of manual intervention and domain knowledge. In 1997, Swanson and Smalheiser [4] developed an interactive system called Arrowsmith that helps users find potentially meaningful linking concepts between a starting concept and a target concept. However, Arrowsmith only performs in a closed discovery process, which means it does not generate new connections for a given starting concept.

Many researchers have successfully replicated Swanson's discoveries using various approaches. Gordon and Lindsay [5, 6] applied several methods of information retrieval, such as token counts, document counts, relative frequencies, and TF*IDF, to LBD. However, the authors exerted much of their effort towards discovering linking concepts instead of target concepts. In addition, their method required further biomedical expertise for application.

Weeber et al. [7] took advantage of a natural language processing tool to identify biomedical concepts and pruned redundant linking concepts and target concepts with use of the UMLS ontology. However, while their method was more automatic than previous works, the authors adopted only a limited number of linking concepts to discover target concepts. They also investigated the potential uses of thalidomide [8].

Hristovski et al. [9] applied the association rules to find hidden related Medical Subject Heading (MeSH) concepts through an open discovery approach. In this approach, support and confidence metrics were used to select concepts of interest based on the co-occurrence of MeSH terms.

Srinivasan [10] introduced a method where only the most important linking concepts within certain semantic types were retained to avoid obtaining vast numbers of target concepts. In this method, when the top linking concept is selected from each semantic type, target words gain relatively high rankings. However, when the second or third top linking concepts are selected, the rankings of target words drop.

Cameron et al. [11] adopted the semantic predications along with structured background knowledge and graph-based algorithms to semi-automatically capture the informative associations. They tested and verified Swanson's Raynaud Syndrome-Fish Oil hypothesis, demonstrating that Swanson's manually intensive techniques can be undertaken semi-automatically.

Cohen et al. [12] presented the Predication-based Semantic Indexing (PSI), a novel distributional model that encodes predications into a vector space, and provides its possible application to literature-based knowledge discovery. Afterward, Cohen et al. [13] extended the PSI approach to search efficiently across triple-predicate pathways, and utilized it to infer double and triple predicate pathways, which were further adopted to guide search for treatments for other diseases.

Methods

Based on Swanson's discovery process, Weeber et al. [7] defined two kinds of knowledge discovery approach, namely, open discovery and closed discovery. An open discovery process is used to generate a hypothesis (Figure 1). For a given starting concept C, concepts that co-occur with C in the literature (called linking concepts B) are found. Concepts that co-occur with linking concepts B (called target concepts A) are then similarly found, bearing in mind that concepts A should not co-occur with the starting concept C. This process can be described as C->B->A.
Figure 1

Open discovery process as defined by Weeber et al.

A closed discovery process is used to test a hypothesis (Figure 2). Let's say that, for two given concepts C and A, a researcher would like to find out whether or not hidden links exist between them. The more links found between A and C, the more likely it is that the tested hypothesis is correct. This process can be described as C->B<-A.
Figure 2

Closed discovery process as defined by Weeber et al.

We adopt the open discovery approach to replicate Swanson's discoveries on Raynaud Syndrome-Fish Oil and Migraine-Magnesium with starting concepts of Raynaud Syndrome and Migraine, respectively. The framework of our open discovery approach is illustrated in Figure 3, and 3the detailed processes are described in the following sections.
Figure 3

Framework of our open discovery approach.

MEDLINE, the primary bibliographic database used in LBD, includes a number of disciplines, such as basic medicine, clinical medicine, experimental medicine, pharmacology, and so on. Figure 4 shows a brief structure of a MEDLINE record, where PMID stands for the unique ID of an article, TI stands for title, AB stands for abstract, and MH stands for MeSH. MeSH, from the National Library of Medicine's controlled vocabulary thesaurus, is selected by experts to index MEDLINE records. Each document is represented by 12 MeSH terms on average; these terms generally consist of terms naming descriptors in a hierarchical structure [14].
Figure 4

Brief structure of a MEDLINE record.

Linking concept selection

For a given starting concept C, all MEDLINE records in the correct time range (discussed in the section of "results and discussion") containing C are retrieved, from which we collect all MeSH terms co-occurring with C as preliminary linking concepts. We find that using only co-occurrence in the MeSH field may be inadequate, since two MeSH terms co-occurring in one MEDLINE record may have no relation. The title and abstract can represent the main idea in biomedical literature. For example, we find over 1800 MeSH terms co-occurring with Raynaud Syndrome in the MeSH field but only 700 MeSH terms in the title and abstract fields. If we use all preliminary linking concepts to discover target concepts, the number of target concepts produced will be vast and a large amount of noise will be introduced. Therefore, pruning redundant linking concepts and selecting the most promising ones in open discovery are very important. In this paper, we select linking MeSH terms using both statistical and textual features. Each linking concept is represented by a vector. We use an SVM classifier to classify these linking terms as positive instances (relevant to the starting concept) or negative ones (irrelevant to the starting concept). The SVM classifier is used because results obtained through only co-occurrence are not comprehensive; the SVM classifier can compute a proper weight for each feature and then integrate all of these features.

Here, we use the Semantic Network where all MeSH terms are assigned to at least one of 135 semantic types. For example, the term Raynaud Syndrome has semantic type Disease or Syndrome and Fish Oil has semantic type Lipid. We choose the same semantic types used by Weeber et al. [7] and Srinivasan [10]. Thus, the semantic types for linking concepts in the present study are Biological function, Cell function, Finding, Molecular function, Organism function, Organ or Tissue function, Pathological function, Phenomenon or process, and Physiological function, all of which involve the functions of the disease. The semantic types for target concepts are Element, Ion, Isotope, Vitamin and Lipid, all of which are dietary factors.

Statistical features

In the proposed method, we use Mutual Information Measure (MIM) [15], which is widely applied to quantify dependencies between co-occurring concepts, to calculate statistical features. The MIM score is calculated as:
M I M ( A , B ) = log 2 ( P A B P A . P B )
(1)

where P AB is the joint probability of term A and B co-occurring in the same document, and P A , P B are the probabilities of observing the term A, B, respectively, in any given document. The statistical features consist of two parts: the MIM score calculated for the MeSH field and the MIM score calculated for the title and abstract fields of MEDLINE records.

Textual features

We select five Boolean features (the value is 1 or 0) to evaluate the degree of connection between two concepts. Taking Raynaud Syndrome as an example, its symptoms are high blood viscosity and high platelet aggregation. Therefore, the interaction words should be "increase" or "aggravate", and so on. Intuitively, two MeSH terms co-occurring in one sentence have a strong connection, so whether or not two MeSH terms co-occur in one sentence of the MEDLINE title and abstract is selected as a Boolean feature. In addition, we establish an interaction word list, which indicates linking concepts may have an influence on the starting concept, to find potentially relevant and promising linking concepts. The interaction word list consists of 115 interaction words and their variants, and the connection is much stronger if two MeSH terms and an interaction word co-occur in one sentence. Therefore, the second Boolean feature we select is whether or not there is an interaction word in the sentence.

In MEDLINE records, some diseases or drug names have abbreviated forms. Usually, at first mention, both the full name and abbreviation appear and only the abbreviation or pronoun is used in subsequent sentences. For example, AD is the abbreviation for Alzheimer's disease. In the first sentence "Alzheimer's disease (AD) is the fourth leading cause...," both Alzheimer's disease and its abbreviation AD appear. In the subsequent sentence "...in the brains of patients with AD are described...," only the abbreviation AD appears. Therefore, we select whether or not two MeSH terms co-occur in two neighboring sentences as a Boolean feature and whether or not there is an interaction word as another Boolean feature. Finally, we select whether or not two MeSH terms co-occur in the MEDLINE abstract field as a Boolean feature.

The following are the seven features we select:
  1. I.

    MIM score calculated for the MeSH field.

     
  2. II.

    MIM score calculated for the title and abstract fields.

     
  3. III.

    Whether or not two MeSH terms co-occur in one sentence.

     
  4. IV.

    Whether or not there is an interaction word in one sentence.

     
  5. V.

    Whether or not two MeSH terms co-occur in two neighboring sentences.

     
  6. VI.

    Whether or not there is an interaction word in two neighboring sentences.

     
  7. VII.

    Whether or not two MeSH terms only co-occur in the MEDLINE abstract field.

     

In our method, each linking concept is represented by the seven features, and an SVM classifier is employed to classify these linking concepts as positive or negative instances. Positive linking concepts will further be used to discover target concepts.

Target concept discovery

We search the MeSH field of MEDLINE records for concepts that co-occur with positive linking concepts. These terms are preliminary target concepts. Target concepts that appear in the set of linking concepts are removed and only target concepts belonging to the semantic types Element, Ion, Isotope, Vitamin or Lipid are retained, namely the final target concepts.

Results and Discussion

Datasets

Swanson found that there might be hidden links between Alzheimer's disease and indomethacin through several linking concepts. In the present paper, we selected linking concepts that had interactions with the starting concept Alzheimer's disease as positive instances. Some of these linking concepts were discovered by Swanson and verified to be useful. Other linking concepts were found in the title and abstract fields, which co-occurred in one sentence or two neighboring sentences with the starting concept Alzheimer's disease and interaction words. Negative instances included three types: MeSH terms that were too general, MeSH terms that did not co-occur in two neighboring sentences, and MeSH terms that co-occurred in one sentence or two neighboring sentences but contained no interaction word. We then calculated the feature vectors for all positive and negative instances. Since the number of linking concepts that have interactions with the starting concept was very limited, we ultimately obtained 40 positive and 40 negative instances.

For each experiment, we retrieved all linking concepts for the given starting concepts and calculated their feature vectors, which composed the test set. We employed the SVM classifier to tag vectors as positive or negative instances. Next, we made use of the positive instances to discover target concepts.

Target concept ranking

Pratt and Yetisgen-Yildiz [16] exploited linking term count (LTC) to rank target concepts (i.e., one target concept is more important if there are more linking terms connecting to it). In the proposed method, we adopted a similar idea but did not simply identify concepts co-occurring in the MeSH field as linking terms. Instead, we retrained those "useful" linking terms defined in two ways. First, we identified linking concepts co-occurring with the target concepts in the title and abstract fields as useful linking concepts. Second, we identified linking concept co-occurring with the target concepts and an interaction word in one sentence as useful linking concepts. Using these steps for verification, spurious target concepts can be pruned and the exact text in which linking and target concepts co-occur can be found.

Performances of features and combinations

The performances of different features and their combinations in Raynaud Syndrome-Fish Oil and Migraine-Magnesium experiments are shown in Table 1. In this section, selected semantic types were not used for filtering because we intended to examine the effect of our supervised learning method. Given that Fish Oil and Magnesium are two known effective concepts for Raynaud Syndrome and Migraine, respectively, linking concepts leading to the target concepts (Fish Oil and Magnesium) in the MeSH field are considered useful linking terms for ranking target concepts. The percentage of useful LTC is adopted to evaluate the performances of these features and calculated as:
Table 1

Performances of different features and their combinations.

Features I

*

*

*

*

*

*

*

Features II

 

*

*

*

*

*

*

Features VII

  

*

*

*

*

*

Features V

   

*

*

*

*

Features VI

    

*

*

*

Features III

     

*

*

Features IV

      

*

percentage of useful LTC (Raynaud Disease-Fish Oil)

21.4%

22.9%

22.2%

25.4%

25.5%

27.7%

29.8%

percentage of useful LTC (Migraine-Magnesium)

79.7%

82.1%

75.5%

76.6%

77.9%

78.2%

85.6%

Note: "*" stands for including of the corresponding feature.

P e r c e n t a g e _ o f _ U s e f u l _ L T C = n u m b e r ( U s e f u l _ L i n k i n g _ T e r m s ) n u m b e r ( A l l _ L i n k i n g _ T e r m s )
(2)

If spurious linking concepts can be pruned to retain relevant linking concepts leading to the determination of a given target concept, the denominator will decrease and the percentage of useful LTC will increase.

Using only Feature I in the Raynaud Syndrome-Fish Oil experiment, the percentage of useful LTC obtained was 21.4%. With the introduction of Feature II, the percentage of useful LTC increased to 22.9%, likely because concepts in the title and abstract fields can reflect the topic more precisely than concepts in the MeSH field. Textual features were added according to the strictness of linking concepts from loose to strong in the order of: VII, V, VI, III, and IV. The percentage of useful LTC decreased slightly when Feature VII was added. However, when the starting and linking concepts were limited to two neighboring sentences, the percentage of useful LTC increased. Interaction words also contributed to the performance of the percentage of useful LTC. After all the textual features were added, the percentage of useful LTC reached a maximum score of 29.8%. As for the ranking of Fish Oil, it was not improved as significantly as the percentage of useful LTC. This is probably because the LTC of Fish Oil is very limited and there are many general target concepts with a large LTC.

In the experiment of Migraine-Magnesium, similar results were obtained, and the percentage of useful LTC increased from 79.7% to 85.6%. Magnesium obtained a No. 1 ranking compared with Pratt and Yetisgen-Yildiz's No. 11 [16].

From Table 1 we can draw some conclusions: 1. Feature II (MIM score calculated for the title and abstract fields) can better reflect the dependencies of two concepts than Feature I (MIM score calculated from the MeSH field). 2. Textual features improve the results obtained with only statistical features. 3. Interaction words contribute to the performance of the percentage of useful LTC.

Since incorporating statistical and textual features can increase the percentage of useful linking concepts and retain relevant linking concepts, we will use these seven features to represent linking concepts in the following experiments.

Raynaud Syndrome-Fish Oil

In the Raynaud Syndrome-Fish Oil experiment, we utilized MEDLINE records obtained prior to 1986 since Swanson [17] made the discovery in this year. The starting concept C is Raynaud Syndrome. We retrieved all MeSH terms co-occurring with C and generated their feature vectors. Then, we employed the SVM classifier to classify them as positive or negative instances.

As can be seen from Table 2 rankings of Fish Oil and percentages of useful linking concepts obtained using positive concepts are much higher than those using all linking concepts. By using positive concepts, many irrelevant linking concepts were excluded, which would otherwise generate many target concepts irrelevant to the starting concept. The LTC of target concepts discovered by these negative linking concepts decreased after pruning, and therefore the ranking of relevant target concepts such as Fish Oil were promoted. Further filtering with the semantic types pushes the ranking of Fish Oil even higher.
Table 2

Experimental results of Raynaud Syndrome-Fish Oi l problem.

Ranking rules

Linking concepts

LTC

Useful LTC

Percentage of useful LTC

Ranking of Fish Oil

Rule 1

all

1852

148

8.0%

141

 

Positive

323

49

15.2%

121

 

all + ST

176

15

8.5%

128

 

positive + ST

47

7

14.9%

80

Rule 2

All

1852

68

3.7%

128

 

Positive

323

28

8.7%

105

 

all + ST

176

8

4.5%

96

 

positive + ST

47

6

12.8%

45

Note: "useful LTC" stands for the number of concepts leading to the finding of Fish Oil; "ST" stands for semantic types filtering.

As aforementioned in the subsection of "target concept ranking", two kinds of "useful" linking terms for ranking target concepts are defined: linking concept co-occurring with the target concepts in the title and abstract fields (Rule 1) and linking concept co-occurring with the target concepts and an interaction word in one sentence as useful linking concepts (Rule 2). The latter is stricter so that more precise but less useful linking concepts are retained and, as experimental results show, the rankings of Fish Oil increase and the percentages of useful linking concepts decrease.

Fish Oil obtained the best ranking (No. 45) by using positive concepts and semantic type filtering in a sentence-level experiment.

Table 3 shows part of the target concepts discovered at the sentence level. Weeber et al. [7] used three pathways (Platelet Aggregation, Blood Viscosity, and Vascular reactivity) identified by Swanson to discover target concepts. In their experiments, Fish Oil ranked 7 and 20 for the pathways Platelet Aggregation and Blood Viscosity, respectively. The pathway Vascular reactivity could not find Fish Oil. In contrast, when all linking concepts were used, as in our experiment, Fish Oil ranked 45 in all target semantic types. Srinivasan [10] ranked the target concepts in each target semantic type and Fish Oil ranked 19 under the semantic type Lipid; in our experiment, it ranked 16.
Table 3

Part of target concepts discovered for Raynaud Syndrome

Target concept

Ranking in target semantic types

Ranking in semantic type Lipid

Carbon

1

 

Fatty Acids

2

1

Nitrogen

3

 

Oil

7

2

...

...

...

Cadmium

26

...

Platelet Activating Factor

27

7

Lipoproteins, VLDL

28

8

...

...

 

Liposomes

32

9

Oleic Acid

33

10

Lipoproteins, LDL

34

11

Lipoproteins, HDL

35

12

...

...

...

Phosphatidylcholines

43

15

Anions

44

 

Fish Oil

45

16

...

...

...

Swanson found three linking concepts through which he discovered the hidden relation between Raynaud Syndrome and Fish Oil. These linking concepts are Vasoconstriction, Blood Viscosity, and Platelet Aggregation. As shown in Table 4 the proposed method was able to classify these concepts as positive instances and adopted them to discover target concepts further. Meanwhile, the number of linking concepts was reduced from 1852 to 323.
Table 4

Comparison of useful linking concepts between Swanson's and our system for the Raynaud Syndrome-Fish Oi l problem

Three useful linking concepts discovered by Swanson

Discovered by our system

Vasoconstriction

Y

Blood Viscosity

Y

Platelet Aggregation

Y

Migraine-Magnesium

In the Migraine-Magnesium experiment, we utilized MEDLINE records prior to 1988 since Swanson [18] made the discovery in this year. The starting concept C adopted was Magnesium.

Using positive instances to discover target concepts, Magnesium obtained a high ranking. Further filtering with the semantic types pushed Magnesium to rank No. 1. Meanwhile, the percentage of useful linking concepts improved. Table 5 shows the results obtained by different experimental methods. In these experiments, the ranking of Magnesium was relatively high and consistently ranked No. 1.
Table 5

Experimental results of Migraine-Magnesiu m problem.

Ranking rules

Linking concepts

LTC

Useful LTC

Percentage of useful LTC

Ranking of Magnesium

Rule 1

All

2898

1405

48.5%

2

 

positive

529

398

75.8%

1

 

all + ST

236

199

84.3%

2

 

positive + ST

61

53

86.9%

1

Rule 2

All

2898

827

28.5%

4

 

positive

529

290

54.8%

2

 

all + ST

236

80

33.9%

5

 

positive + ST

61

39

63.9%

1

Note: "useful LTC" stands for the number of words leading to the finding of Magnesium; "ST" stands for semantic type filtering.

Table 6 shows part of the target concepts we discovered at the sentence level. In Srinivasan's method [10], the best rankings obtained for Magnesium were 5 and 12 when linking terms ranking highest and second highest within each semantic type were adopted. In Pratt and Yetisgen-Yildiz's method [16], the LTC was 29 and the best ranking of Magnesium was 11. In our experiment, Magnesium obtained a rank of No. 1 with an LTC of 39.
Table 6

Part of top ranking target concepts discovered for Migraine

Target concept

LTC

Ranking in target semantic types

Ranking in semantic type Element, Ion, or Isotope

Carbon

39

1

1

Magnesium

39

1

1

Ions

37

2

2

Nitrogen

34

3

3

Oil

32

4

 

Hydrogen

31

5

4

Fats

28

6

5

Iodine

24

7

6

...

...

...

...

Table 7 lists eleven complementary arguments that connect magnesium deficiency with migraine, as discovered by Swanson et al. [19]. The proposed method was able to classify 9 of these arguments as positive instances. Although the proposed method missed two useful linking concepts, it reduced the number of linking concepts from 2898 to 529.
Table 7

Comparison of useful linking concepts between Swanson's and our system for the Migraine-Magnesiu m problem

Eleven useful linking concepts discovered by Swanson

Discovered by our system

Stress

Y

Vascular Tone

Y

Calcium Channel Blockers

Y

Spreading Cortical Depression

N

Epilepsy

Y

Platelet Aggregation

Y

Serotonin

Y

Substance P

Y

Prostaglandins

Y

Inflammation

Y

Hypoxia

N

Conclusions

In the field of literature-based hidden knowledge discovery, popular methods based on co-occurrence produce too many target concepts, leading to the declining ranking of potentially relevant target concepts. In the current paper, we propose a new method for choosing useful and promising linking concepts. This method selects statistical and textual features and employs an SVM model to classify linking concepts whether relevant to the starting concepts. Linking concepts classified as relevant to the starting concept are adopted to further find target concepts.

The current experimental results on two classic experiments, Raynaud Syndrome-Fish Oil and Migraine-Magnesium, show that the ranking of potentially relevant target concepts is promoted by adopting only relevant linking concepts. In addition, we employ the percentage of useful LTC to evaluate the performance of the proposed method. The results show that the percentage of useful LTC is significantly improved. The proposed system is more automatic than other methods, with only required manual step of the training set construction. Thus, the proposed method has the potential to help biomedical experts find the most useful and valuable target concepts effectively. In future research, we aim to find more useful linking concept features to gain better results.

Declarations

Declarations

Publication of this article has been funded by the Natural Science Foundation of China (No. 60673039, 60973068, 61277370, 61070098, 61272373), the National High Tech Research and Development Plan of China (No. 2006AA01Z151), Natural Science Foundation of Liaoning Province, China (No. 201202031), State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002).

This article has been published as part of BMC Bioinformatics Volume 15 Supplement 12, 2014: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S12.

Authors’ Affiliations

(1)
Information Retrieval Laboratory, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology

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© Cheng et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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