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High throughput analysis and alerting of disease outbreaks from the grey literature


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Part of a presentation a made to the EBI recently on my current research in infectious disease alerting and biogeographic mapping using BioCaster.

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High throughput analysis and alerting of disease outbreaks from the grey literature

  1. 1. High throughput analysis and alerting of disease outbreaks from the grey literature Nigel Collier Associate Professor National Institute of Informatics, Tokyo [email_address]
  2. 2. Background <ul><li>Short bio </li></ul><ul><ul><li>Associate Professor (NII, Japan), 00 - </li></ul></ul><ul><ul><li>JSPS Fellow (Tokyo), 98 - 00 </li></ul></ul><ul><ul><li>Toshiba Fellow (Toshiba Corp.), 96 - 98 </li></ul></ul><ul><ul><li>PhD Language Engineering (UMIST), 93 - 96 </li></ul></ul><ul><ul><li>BSc Computer Science (Leeds), 88 - 92 </li></ul></ul><ul><li>Research themes </li></ul><ul><ul><li>Application and analysis of algorithms for text mining and knowledge acquisition </li></ul></ul>
  3. 3. Text mining <ul><li>Fact/event extraction from large scale text collections </li></ul><ul><li>Fusion of technologies </li></ul><ul><ul><li>NLP, Machine Learning, Ontologies, Reasoning </li></ul></ul>
  4. 4. Projects [1] Collier, N. et al ., &quot;The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers&quot;, in Proc. of the Annual Meeting of the European Association for Computational Linguistics (EACL-99), pp.271-272, Norway , 1999. [2] Mizuta, Y. et al . (2006), “Zone analysis in biology articles as a basis for information extraction”, International Journal of Medical Informatics, Elsevier, Vol. 75, Issue 6, pp. 468-487. [3] Collier, N. et al . (2007), &quot;Detecting Web rumours with a multilingual ontology supported text classification system&quot;, Advances in Disease Surveillance, vol. 4, pp. 242. 1998 2000 2002 2004 2006 2008 2010 GENIA PIA ZAISA BioCaster Support for database curation & resource building (PI:Tsujii) Linking biomedical annotations to SW ontologies Locating biomedical results in full research papers Early detection and alerting of PH threats
  5. 5. Outline <ul><li>Research context </li></ul><ul><li>Theme 1: Text mining system </li></ul><ul><li>Theme 2: Multilingual ontology </li></ul><ul><li>Theme 3: Early alerting </li></ul><ul><li>Future developments </li></ul>
  7. 7. Alerting real world events Cholera, 2007, Iraq 1. Real world event 2. Grey literature response 4. Detecting unusual events News volume Time 3. Text mining on unstructured news Alert level 5. Issue alert
  8. 8. Globalization: a problem in one location is everybody’s headache … . + many hundreds more each year Plague, 2005, DRC Nipah, 1998, Malaysia Anthrax, 2001, USA SARS, 2003 ,HK Cholera, 2007, Iraq H5N1 flu, 2003- Ebola, 2007, DRC Foot & mouth, 2001, UK
  9. 9. Early detection Timely intervention <ul><li>Chance to reduce: </li></ul><ul><li>severity of the outbreak </li></ul><ul><li>length of the outbreak </li></ul><ul><li>social & economic cost </li></ul>Source: 2008 May report of the Auditor General of Canada: Chapter 5 – Surveillance of Infectious Diseases – Public Health Agency of Canada
  10. 10. Typical operational flow Local field workers GP reports EMS visits Over the counter sales Grocery sales School absentees Reference labs Local labs Chief medical officer/Cabinet Timeliness Rumours WHO Disease Control Centre See also: [4] Mandl K. D. et al. (2004), “Implementing syndromic surveillance: a practical guide informed by early experience”, Journal of the American Medical Informatics Association, vol. 11, no. 2, Mar/Apr 2004, pp. 141-150.
  11. 11. Trans-national issues in health security <ul><li>International Health Regulations – IHR 2005 </li></ul><ul><ul><li>Mandatory notification of PHEICs (public health emergency of international concern) within a stipulated time period </li></ul></ul><ul><ul><li>Core public health capacity required in health surveillance </li></ul></ul><ul><li>Economic pressure </li></ul><ul><ul><li>Spending priorities of national governments lead to gaps </li></ul></ul><ul><ul><li>Patchy levels of training </li></ul></ul><ul><ul><li>Different perceptions of disease risks </li></ul></ul><ul><li>Political pressure </li></ul><ul><ul><li>Reluctance to report due to economic risks </li></ul></ul><ul><ul><li>Reluctance to share samples due to economic benefits </li></ul></ul>
  12. 12. BioCaster <ul><li>Federated access to heterogeneous textual resources </li></ul><ul><li>Automatic annotation of resources according to an open access computable semantics </li></ul><ul><li>Quick and intuitive access to information </li></ul><ul><li>Data analysis and exploration tools </li></ul><ul><li>Mechanisms for user annotation </li></ul><ul><li>Goal: </li></ul><ul><ul><li>Use linguistic signals in the grey literature to sensitize the right people at the right time to a potentially significant public health hazard; </li></ul></ul><ul><ul><li>Supplement other traditional data sources </li></ul></ul>
  13. 13. Grey literature <ul><li>Advantages </li></ul><ul><ul><li>Cheaply available source </li></ul></ul><ul><ul><li>1000’s of news/blog/reports published each day </li></ul></ul><ul><ul><li>Near real time reporting of local news </li></ul></ul><ul><ul><li>Includes independent as well as official sources </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Little tolerance for mistakes: experts demand well informed and timely reports </li></ul></ul><ul><ul><li>Significant challenges: ambiguity, vagueness, scale, multilinguality </li></ul></ul><ul><ul><li>Not obvious (yet) to machines what is unusual </li></ul></ul><ul><ul><li>Few groups with the necessary cross-disciplinary expertise </li></ul></ul>Free Text
  14. 14. Typical operational flow [2] Local field workers GP reports EMS visits Over the counter sales Grocery sales School absentees Reference labs Local labs Chief medical officer/Cabinet Timeliness Rumours WHO Disease Control Centre Grey literature analysis
  15. 15. Detecting the ‘unusual’ <ul><li>What is unusual? </li></ul><ul><ul><li>A new outbreak of Ebola in Uganda? </li></ul></ul><ul><ul><li>A Chikungunya outbreak in Indonesia? … in Italy? </li></ul></ul><ul><ul><li>Demonstrations for yellow fever vaccines in Paraguay? </li></ul></ul><ul><ul><li>A Norwalk-virus outbreak closing a hospital in Glasgow? </li></ul></ul><ul><ul><li>An XDR-Tuberculosis traveler from Somalia arrives in Scotland? </li></ul></ul><ul><ul><li>A mystery illness among drug addicts in an Irish city? </li></ul></ul><ul><ul><li>Eastern equine influenza in Florida? </li></ul></ul><ul><ul><li>Salmonellosis in Colorado’s domestic water supply? </li></ul></ul><ul><li>A constantly shifting baseline </li></ul><ul><ul><li>Some events are always noteworthy (see IHR) </li></ul></ul><ul><ul><li>For others it depends a great deal on local context and the time at which you catch them </li></ul></ul>
  16. 16. Existing systems * non-governmental systems GPHIN ProMed* Argus MedISys BioCaster* Location Canada USA USA EU Japan Automatic? Automatic & Manual Manual volunteers Automatic & Manual Automatic Automatic Language coverage UN official languages All? ~36? EU official languages Asia-Pacific (en,jp,th,vn,sp,zh…) Access Closed Open Closed Closed Mixed Open source? No NA No No Yes
  17. 17. THEME 1: TEXT MINING SYSTEM Funding source: [A] JSPS grant in aid for scientific research on priority areas (18049071), PI, 4/2006-3/2007 [B] JSPS grant in aid for scientific research : Young researcher award category A (18680015), PI, 4/2006-3/2008
  18. 18. BioCaster system overview
  19. 19. High throughput infrastructure Dedicated power, cooling, very high speed networking and high level of security Physically located at NII’s Chiba annex 1 hour from Tokyo <ul><li>Rack mounted cluster computer </li></ul><ul><ul><li>2 racks </li></ul></ul><ul><ul><li>16 nodes (32 Xeon CPUs) </li></ul></ul><ul><ul><li>1 Snap server NAS </li></ul></ul><ul><ul><li>1 Netgear NAS </li></ul></ul><ul><ul><li>1 Netgear firewall </li></ul></ul>
  20. 20. The nature of the task: ambiguity Confusion South Sudan hit by Ebola-like fever Zika virus in Micronesia ( Yap ) Vista attacked by 13-year old virus Obama fever builds as black Americans await a new era Undiagnosed disease in Java Bird flu outbreak drill spooks Manitoba town Boredom causing outbreaks of petrol sniffing
  21. 21. <ul><li>Terminology explosion is endemic to this task: </li></ul><ul><ul><li>Influenza A virus subtype H5N1 </li></ul></ul><ul><ul><li>Avian influenza H5N1 virus </li></ul></ul><ul><ul><li>H5N1 bird flu virus </li></ul></ul><ul><ul><li>H5N1 avian influenza virus </li></ul></ul><ul><ul><li>Avian H5N1 virus </li></ul></ul><ul><ul><li>A(H5N1) </li></ul></ul><ul><ul><li>H5N1 virus </li></ul></ul><ul><ul><li>H5N1 avian flu virus </li></ul></ul><ul><ul><li>H5N1 flu virus </li></ul></ul><ul><ul><li>Highly pathogenic H5N1 virus </li></ul></ul><ul><ul><li>… .etc. </li></ul></ul><ul><li>Unconventional forms appear surprisingly often in the news: </li></ul><ul><ul><li>H5N1 birdflu virus </li></ul></ul><ul><ul><li>Mexican pig flu </li></ul></ul><ul><li>Needs to be grounded in a domain ontology </li></ul>The nature of the task: uncontrolled vocabularies
  22. 22. Entity analysis <ul><li>Named entity study </li></ul><ul><ul><li>Conducted a study based on 500 positively classified texts to explore NER. 10-fold cross validation on SVM. 17361 phrases processed, 12168 correct. Total accuracy 88.7%, F-score 71.1 </li></ul></ul>Target >80
  23. 23. Entity analysis issues <ul><li>Often need to correct mistakes or handle new terms on the fly </li></ul><ul><ul><li>No time for retagging and retraining (e.g. pandemic H1N1) so moved to a rule based approach. </li></ul></ul><ul><li>The accuracy scores hide trivial boundary errors </li></ul><ul><ul><li>‘ [a] 24-year-old man’ – in reality useability is much higher after cleaning. </li></ul></ul><ul><li>Systematic polysemy between location and organization </li></ul><ul><ul><li>‘ Indonesia announced…’ vs </li></ul></ul><ul><ul><li>‘ Outbreak in Indonesia’ </li></ul></ul><ul><li>Some tricky traditional ambiguity problems </li></ul><ul><ul><li>‘ German measles cases have risen’  rubella virus or measles virus? </li></ul></ul>
  24. 24. Bio or Non-Bio? <ul><li>Topic classification study [4] </li></ul><ul><ul><li>Conducted a study based on 1000 annotated texts to explore topic classification across mixed genres and topics </li></ul></ul><ul><ul><li>Accuracy 88.1% </li></ul></ul><ul><li>Borderline false positives </li></ul>[4] Doan, S., Kawazoe, A. and Collier, N. (2007), &quot;The Role of Roles in Classifying Annotated Biomedical Texts&quot;, in proceedings of BioNLP 2007, Prague, Czech Republic, June, pp. 17-24.
  25. 25. Event capture using a regular expression language (SRL) [5] McCrae, J., Conway, M. and Collier, N. (2009), “Simple Rule Language editor and handbook”, available from
  26. 26. Single slot relations [1] disease(D);species(“human”) :- PERSON(P,matches(@victim)) skipwords(2) DISEASE(D) species human disease measles species human disease chickenpox
  27. 27. Single slot relations [2] <ul><li>Extensive word list groups </li></ul><ul><ul><li>Verb groups (e.g. scare, injest, mutatate) </li></ul></ul><ul><ul><li>Organization function groups (e.g. social, economic production) </li></ul></ul><ul><ul><li>Person function groups (e.g. victim, medical, tourist) </li></ul></ul><ul><ul><li>Country provinces (from BCO) </li></ul></ul><ul><ul><li>Country adjectives </li></ul></ul><ul><ul><li>Pathogens (from BCO) </li></ul></ul><ul><ul><li>Species adjectives </li></ul></ul><ul><li>Rule types </li></ul><ul><ul><li>Province  Country (BCO) </li></ul></ul><ul><ul><li>Pathogen  Disease (BCO) </li></ul></ul><ul><ul><li>Airport  City </li></ul></ul><ul><ul><li>A-list of pathogens (BCO + supplements) </li></ul></ul><ul><ul><li>Syndrome  Disease (BCO) </li></ul></ul><ul><ul><li>Simple temporal rules </li></ul></ul><ul><ul><li>Alerting rules for specific events (international travel, drug resistance, food contamination, farm worker disease…) </li></ul></ul>
  28. 28. Multi-slot frame <ul><li>Typed slots </li></ul><ul><li>Disease,country,province,agent linked to BioCaster ontology </li></ul><ul><li>Simple distinction between animals and humans </li></ul><ul><li>Boolean switches for key alerts (deliberate release, zoonosis etc.) </li></ul><EVENT name=”OUTBREAK”> <SLOT name=”HAS_DISEASE” type=”DISEASE” content=”” alt=”” root_term=”” bid=””/> <SLOT name=”HAS_LOCATION.COUNTRY” type=”LOCATION” content=”” alt=”” root_term=”” bid=””/> <SLOT name=”HAS_LOCATION.PROVINCE” type=”LOCATION” content=”” alt=”” root_term=”” bid=””/> <SLOT name=”HAS_LOCATION.OTHER” type=”LOCATION” content=””/> <SLOT name=”HAS_AGENT” type=”micro_organism” content=”” alt=”” root_term=”” bid=””/> <SLOT name=”HAS_SPECIES” type=”animal” content=””/> <SLOT name=”TIME.relative” type=”string” content=””/> # Historical, Recent_Past,Present,Hypothetical <SLOT name=”INTERNATIONAL_TRAVEL” type=”Boolean” content=””/> <SLOT name=”DELIBERATE_RELEASE” type=”Boolean” content=””/> <SLOT name=”ZOONOSIS” type=”Boolean” content=””/> <SLOT name=”DRUG_RESISTANCE” type=”Boolean” content=””/> <SLOT name=”FOOD_CONTAMINATION” type=”Boolean” content=””/> <SLOT name=”HOSPITAL_WORKER” type=”Boolean” content=””/> <SLOT name=”FARM_WORKER” type=”Boolean” content=””/> <SLOT name=”MALFORMED_PRODUCT” type=”Boolean” content=””/> <SLOT name=”NEW_TYPE_AGENT”> type=”Boolean” content=””/> <SLOT name=”COMMENT” type=”string” content=””/> </EVENT>
  29. 29. Multi-slot challenge Is the disease outbreak ongoing or historical? Is there an outbreak ongoing now in Switzerland? San Diego measles outbreak In San Diego Measles, one of the most contagious diseases, has infected 11 children… Over the past month officials have tracked … The latest victim is an 8-year-old who may have spread the virus during a visit to Whole Foods Market in Hillcrest and later to a Cirque du Soleil performance… This is the most measles cases in the city in 17 years … The outbreak is believed to have started with a child who caught measles in Switzerland , then returned to the United States .
  30. 30. Multi slot relations disease measles city San Diego country United States species human Simple discourse rules OUTBREAK has_disease: measles has_location_country: United States has_location_province: California has_agent: rubeola virus has_species: human international_travel: true
  31. 31. Health alerts Here is the lastest BioCaster post for All diseases in combination with United Kingdom Avian influenza update: two further swans positive for H5N1 - Media Newswire (press release) Date: 2008-02-15 Source: Google News URL: <ul><li>Disease </li></ul><ul><li>Country/region </li></ul><ul><li>Species (animal/human) </li></ul><ul><li>International travel </li></ul><ul><li>Deliberate release </li></ul><ul><li>Zoonosis </li></ul><ul><li>Drug resistance </li></ul><ul><li>Food contamination </li></ul><ul><li>Hospital worker </li></ul><ul><li>Farm worker </li></ul><ul><li>Malformed product </li></ul><ul><li>New type agent </li></ul>
  32. 32. Establishing task evaluation metrics <ul><li>Timeliness – reporting against a human standard (WHO, ProMed) </li></ul><ul><li>Alerting F-score against a gold standard </li></ul><ul><li>Actionable alerts </li></ul><ul><li>Geographic representativeness </li></ul>
  33. 33. Challenges: non-events <ul><li>Sometimes a whole article but often mixed in with positive articles </li></ul><ul><li>Historic events </li></ul><ul><ul><li>“ Living with Enza: The forgotten story of Britain and the Great Flu Pandemic of 1918” </li></ul></ul><ul><li>Speculative events </li></ul><ul><ul><li>“ Influenza pandemic remains a concern” </li></ul></ul><ul><li>Hoaxes </li></ul><ul><ul><li>“ New white powder scare hits Mesa bank” </li></ul></ul><ul><li>Negative findings </li></ul><ul><ul><li>“ Mystery illness not airborne, says hospital” </li></ul></ul>
  34. 34. Challenges: pre-diagnostic data <ul><li>Pre-diagnostic data is often the earliest available: </li></ul><ul><ul><li>“ Up to 144 people being traced over mysterious illness” </li></ul></ul><ul><ul><li>“ Blister-causing virus strikes schoolchildren” </li></ul></ul><ul><ul><li>“ Mystery affliction: Vanadzor residents suffer illness of unknown origin” </li></ul></ul><ul><ul><li>“ Student being treated for possible case of Legionnaire’s” </li></ul></ul><ul><ul><li>“ Unknown fever claims one, Jagastinghpur tense” </li></ul></ul><ul><li>How should this be modeled/filtered? </li></ul><ul><li>Linked to issues in trustworthiness of the news source. </li></ul>
  35. 35. Global health monitor Key figures: >1900 news sources >9000 news reports analysed/day and
  36. 36. THEME 2: MULTILINGUAL ONTOLOGY Funding source: [C] Trandisciplinary integration center project fund from ROIS, PI, 4/2006-3/2008 [D] JST Sakigake grant in aid for scientific research , PI, 10/2008-9/2011
  37. 37. The BioCaster Ontology <ul><li>Purpose: </li></ul><ul><ul><li>To describe the terms and relations necessary to detect and risk assess public health events in the grey literature; </li></ul></ul><ul><ul><li>To bridge the gap between the (multilingual) grey literature and existing standards in biomedicine; </li></ul></ul><ul><ul><li>To be freely available </li></ul></ul>
  38. 38. [9] Kawazoe, A., Chanlekha, H., Shigematsu, M. and Collier, N. (2008), “Structuring an event ontology for disease outbreak detection”, in BMC Bioinformatics (in press). [10] Collier, N., Kawazoe, A., Jin, L., Shigematsu, M., Dien, D. Barrero, R., Takeuchi , K.and Kawtrakul, A. (2007), “A multilingual ontology for infectious disease surveillance: rationale, design and challenges”, Language Resources and Evaluation, Elsevier, DOI: 10.1007/s10579-007-9019-7. A closer look
  39. 39. Example BCO classes* Language ar en fr id ja ko ma ru sp th vi zh Link ICD10 (232) ICD9 (185) LOINC (316) MeSH (1119) MedDRA (218) NCIMetaThesaurus (338) OIE (18) PHAC (53) Pathport (34) SNOMED CT (485) Wikipedia (685) Term arabicTerm (968) englishTerm (4113) frenchTerm (1281) indonesianTerm (1081) japaneseTerm (2077) koreanTerm (1176) malaysianTerm (1001) russianTerm (1187) spanishTerm (1171) thaiTerm (1485) vietnameseTerm (1297) chineseTerm(1142) Diseases AvianDisease (22) BeeDisease (6) BovineDisease (24) CanineDisease (4) CaprineDisease (14) CervineDisease (2) EquineDisease (17) FelineDisease (4) FishDisease (2) HumanDisease (216) LaomorphDisease (2) Non-humanPrimateDisease (16) OtherDisease (2) RodentDisease (8) SwineDisease (12) * Figures in brackets indicate current number of individuals
  40. 40. Simple rules to fill in the gaps <ul><li>Part-whole </li></ul><ul><ul><li>A disease outbreak in Hanoi implies a disease outbreak in Vietnam </li></ul></ul><ul><li>Causation </li></ul><ul><ul><li>Violent coughing fits and fever may imply a possible respiratory disease agent such as influenza. </li></ul></ul><ul><ul><li>Burkholderia pseudomallei can cause Melioidosis </li></ul></ul><ul><li>Synonymy </li></ul><ul><ul><li>H5N1 avian influenza = H5N1 bird flu = A(H5N1) = … </li></ul></ul><ul><ul><li>? unidentified disease = mystery illness </li></ul></ul><ul><li>Cross-language equivalence </li></ul><ul><ul><li>fatigue (en) = 疲労 (ja) = 心智衰弱 (zh) = 피곤 (ko) = อาการอ่อนล้า (th) = Mệt nhọc (vi) </li></ul></ul>
  41. 41. Ontology construction and maintenance <ul><li>Bridging the expert-layman divide [5] </li></ul><ul><li>A formal method for the design of ontologies in text mining [6] </li></ul><ul><li>A quantitative approach to ontology evaluation [7] </li></ul><ul><li>Assisted techniques for ontology population [8] </li></ul>[5] Collier, N., et al. (2007), “A multilingual ontology for infectious disease surveillance: rationale, design and challenges”, Language Resources and Evaluation, Elsevier, DOI: 10.1007/s10579-007-9019-7. [6] Kawazoe, A. et al. (2008), “Structuring an event ontology for disease outbreak detection”, in BMC Bioinformatics, 9 (Suppl 3): S8, DOI: 10.1186/1471-2105-9-S3-S8. [7] Kawazoe, A. et al. “The development of a schema for the annotation of terms in the BioCaster disease detection/tracking system” invited paper for a special edition of the journal of Applied Ontology. [8] McCrae, J. and Collier, N. (2008), “Synonym set extraction from the biomedical literature by lexical discovery”, in BMC Bioinformatics, 9:159, DOI: 10.1186/1471-2105-9-159.
  42. 42. Applying OntoClean to the BCO <ul><li>Q: Why is the top level of performance in bioNER so low? Is it the schema? </li></ul><ul><li>Method: Apply Guarino and Welty’s meta-properties (rigidity, identity, dependency) to clean a task-based NER schema [11]. </li></ul><ul><li>Data: Tested on two versions of the BioCaster news corpus </li></ul><ul><li>Outcome: a measureable improvement +3 F-score from a clear separation of types and roles [12] </li></ul>[11] Guarino N, Welty C. Ontological analysis of taxonomic relations. Lander A, Storey V (eds.) Proceedings of ER-2000: The International Conference on Conceptual Modeling, vol. 1920, 210-224, Springer Verlag LNCS, Berlin, Germany. [12] Kawazoe, A., Jin, L., Shigematsu, M., Barrero, R., Taniguchi, K. and Collier, N. (2006), &quot;The development of a schema for the annotation of terms in the BioCaster disease detection/tracking system&quot;, Proceedings of the KR-MED 2006, Baltimore, Maryland, USA.
  43. 43. THEME 3: EARLY ALERTING Funding source: [D] JST Sakigake grant in aid for scientific research , PI, 10/2008-9/2011
  44. 44. Overview <ul><li>We focus on the relationships that were identified between the disease and its location in the news to find alerting events </li></ul><ul><li>Input: a stream of news articles tagged with disease and country location using the BioCaster system </li></ul><ul><li>Output: a binary alert value: 0 = non-alert, 1 = alert </li></ul><ul><li>Algorithms: temporal aberration detection heuristics used in the public health community </li></ul>
  45. 45. Basic approach Frequencies for daily disease- country counts are compared against the 7 day history; An abnormal trend signals a possible alert Possible alerts are compared against human gold standard alerts to calculate accuracy
  46. 46. Automatic alerting in action at
  48. 48. Future work <ul><li>How can we do fine-grained geo-entity tagging and grounding? </li></ul><ul><li>What are the best rule induction and revision strategies? </li></ul><ul><li>How can we link to the formal literature and make more active use of extant biomedical ontologies? </li></ul>
  49. 49. Many thanks to <ul><li>National Institute of Informatics, Japan </li></ul><ul><ul><li>Dr. Son Doan (now at Vanderbilt University) </li></ul></ul><ul><ul><li>Dr. Ai Kawazoe (now at Tsuda University) </li></ul></ul><ul><ul><li>Dr. Mike Conway (now at Pittsburgh University) </li></ul></ul><ul><ul><li>Dr. John McCrae </li></ul></ul><ul><ul><li>Ms. Hutchatai Chanlekha </li></ul></ul><ul><ul><li>Ms. Qi Wei </li></ul></ul><ul><li>Vietnam National University, Viet Nam </li></ul><ul><ul><li>Dr. Dinh Dien </li></ul></ul><ul><ul><li>Mr. Quoc Hung </li></ul></ul><ul><li>Kasetsart University and NECTEC, Thailand </li></ul><ul><ul><li>Dr. Asanee Kawtrakul </li></ul></ul><ul><li>Okayama University, Japan </li></ul><ul><ul><li>Dr. Koichi Takeuchi </li></ul></ul><ul><li>Funding support </li></ul><ul><ul><li>Japan Society for the Promotion of Science, ROIS, Japan Science and Technology Agency (JST) </li></ul></ul>
  50. 50. THANK YOU <ul><ul><li>Further information at </li></ul></ul>