Major heretofore intractable biotic constraints to African food security that may be amenable to novel biotechnological solutions

The input costs of pesticides to control biotic constraints are often prohibitive to the subsistence farmers of Africa and seed based solutions to biotic stresses are more appropriate. Plant breeding has been highly successful in dealing with many pest problems in Africa, especially diseases, but is limited to the genes available within the crop genome. Years of breeding and studying cultural practices have not always been successful in alleviating many problems that biotechnology may be able to solve. We pinpoint the major intractable regional problems as: (1) weeds: parasitic weeds (Striga and Orobanche spp.) throughout Africa; grass weeds of wheat (Bromus and Lolium) intractable to herbicides in North Africa; (2) insect and diseases: stem borers and post-harvest grain weevils in sub-Saharan Africa; Bemesia tabaci (white fly) as the vector of the tomato leaf curl virus complex on vegetable crops in North Africa; and (3) the mycotoxins: fumonisins and aflatoxins in stored grains. Abiotic stresses may exacerbate many of these problems, and biotechnological alleviations of abiotic stress could partially allay some predicaments. Some of these constraints are already under study using biotechnological procedures, but others may require longer-term research and development to alleviate the problems. Despite the huge impacts of post-harvest weevils and of mycotoxins in grains, these issues had not been given high priority in national biotechnological programs, possibly due to a lack of knowledge of their immensity. The need for public sector involvement is accentuated for cases where immediate profits are not perceived (e.g. lowering mycotoxin levels in farmer utilized grain, which does not increase yield) but where the public weal will gain, and will be invaluable, especially where the private sector supplies genes already isolated

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

DTREEv2, a computer-based support system for the risk assessment of genetically modified plants

Risk assessment of genetically modified organisms (GMOs) remains a contentious area and a major factor influencing the adoption of agricultural biotech. Methodologically, in many countries, risk assessment is conducted by expert committees with little or no recourse to databases and expert systems that can facilitate the risk assessment process. In this paper we describe DTREEv2, a computer-based decision support system for the identification of hazards related to the introduction of GM-crops into the environment. DTREEv2 structures hazard identification and evaluation by means of an Event-Tree type of analysis. The system produces an output flagging identified hazards and potential risks. It is intended to be used for the preparation and evaluation of biosafety dossiers and, as such, its usefulness extends to researchers, risk assessors and regulators in government and industry