To assess the correlation between the burden of seven priority neglected tropical diseases (NTDs) included in the Brazilian National Agenda of Priorities in Health Research – tuberculosis, Chagas disease, leprosy, malaria, leishmaniasis, dengue and schistosomiasis – and their respective research funding and output.
This retrospective review obtained data on disease burden from the Global Burden of Disease Study and funding data from open access sources. Publications were retrieved from Scopus and SciELO, and characterised according to the type of research conducted. Correlation between funding, research output and burden was assessed by comparing the ‘expected’ and ‘observed’ values for funding and publications relative to the proportional burden for each disease.
There was an emphasis in basic biomedical research (average 30% of publications) and a shortage of health policy and systems (average 7%) and social sciences research (average 3%). Research output and funding were poorly correlated with disease burden. Tuberculosis, Chagas disease and schistosomiasis accounted for more than 75% of total NTD‐related DALYs, but accounted for only 34% of publications. Leprosy, leishmaniasis and malaria, together, received 49% of NTD‐related funding despite being responsible for only 9% of DALYs.
The analysis evidenced a lack of correlation between disease burden, research output and government funding for priority NTDs in Brazil. Our findings highlight the importance of monitoring health needs, research investments and outputs to inform policy and optimise the uptake of evidence for action, particularly in developing countries, where resources are scarce and the research capacity is limited. The results contribute to health policy by highlighting the need for improving coordination of scientific activities and public health needs for effective impact.
Author(s): Bruna de Paula Fonseca, Priscila Costa Albuquerque, Fabio Zicker
Organization(s): Oswaldo Cruz Foundation (Fiocruz)
Source: Tropical Medicine & International Health
Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques through the integration of intelligent bibliometrics—e.g., co-occurrence analysis is used for profiling research topics/domains and identifying key players, and recommending potential collaborators based on the incorporation of a link prediction approach; an approach of scientific evolutionary pathways is exploited to trace the evolution of research topics; and a search engine incorporating with fuzzy logics, word embedding, and genetic algorithm is developed for knowledge searching and ranking. Aiming to examine and demonstrate the reliability of the proposed framework, a case of gene-related cardiovascular diseases is selected, and a knowledge base is constructed, with the validation of domain experts.
For FULL-TEXT https://doi.org/10.2991/ijcis.d.200728.001
Author(s): Yi Zhang, Mengjia Wu, Hua Lin, Steven Tipper, Mark Grosser, Guangquan Zhang, Jie Lu
Organization(s): University of Technology Sydney, 23 Strands
Source: International Journal of Computational Intelligence Systems
Patients with kidney failure can only survive with some form of kidney replacement (transplant or dialysis). Unfortunately, innovations in kidney replacement therapy lag behind many other medical fields. This study compiles expert opinions on candidate technologies for future kidney replacement therapies. A worldwide web‐based survey was conducted with 1,566 responding experts, identified via a text-mining process of scientific publications on kidney (renal) replacement therapy, indexed in the Web of Science Core Collection (period 2014‐2019). Candidate innovative approaches were categorized in line with the Kidney Health Initiative roadmap for innovative kidney replacement therapies. Most respondents expected a revolution in kidney replacement therapies: 68.59% before 2040 and 24.85% after 2040, while 6.56% expected none. Approaches anticipated as most likely were implantable artificial kidneys (38.6%) and wearable artificial kidneys (32.4%). A majority of experts expect that kidney replacement therapies can be significantly improved by innovative technologies.
Author(s): Bernardo Pereira Cabral, Joseph V. Bonventre , Fokko Wieringa , Fabio Batista Mota
Organization(s): Oswaldo Cruz Foundation, Harvard Medical School, Maastricht University
Source: Artificial Organs Year: 2020
This study aims at reviewing the articles on the themes of manufacturing strategy (MS) published in “Benchmarking: An International Journal (BIJ)” and investigating the trends of publication for future research. Five-stage methodology to conduct a literature review is adopted comprising: (1) article collection, (2) inclusion/exclusion criteria, (3) reviewing the articles, (4) analyzing the articles and (5) future research directions. A total of 57 articles specific to MS domain published in BIJ are reviewed. Further, a bibliometric analysis comprising keywords co-occurrence, citation and co-citation using a VOSviewer software followed by content analysis using VantagePoint software to analyze the type of research, type of industry and type of tool/method used is carried out. The study helps to find the scope of the journal and research gaps in the MS domain to provide future research directions. Most of the work found is survey-based or case-based in nature. However, there is a need for empirical research to be done in the field of MS.The study facilitates researchers willing to publish in BIJ to understand different themes of accepted papers concerning MS domain. The identified research gaps and future research direction can motivate researchers and practitioners to coin new approaches in the MS domain. A comprehensive review and analysis of the MS literature published in BIJ has been provided. To the best of authors’ knowledge, the current study is the only review study in MS domain focusing on one specific journal.
Author(s): Vishwas Dohale, Angappa Gunasekaran, Milind M. Akarte, Priyanka Verma
Organization(s): National Institute of Industrial Engineering/India, California State University Bakersfield
Source: Benchmarking: An International Journal
The Coronavirus Disease 2019 (COVID-19) pandemic presents a great public health challenge around the world, especially given the urgency to identify effective drugs and develop a vaccine in a short period of time. Globally, there are several drug and vaccine candidates currently in clinical trials, yet it is not yet clear which will prove successful. This study addresses this gap by mapping the treatments and vaccine candidates currently in clinical trials and assessing the opinions on these candidates of virus-related researchers from all over the world. Clinical trial data were obtained from ClinicalTrials.gov and the survey’s respondents were authors of recent scientific publications related to viruses, SARS virus, coronavirus, and COVID-19 indexed in the Web of Science Core Collection. The results show that remdesivir, immunoglobulin from cured patients and plasma are considered the most promising treatments, and ChAdOx1 and mRNA-1273 the most promising vaccine candidates. They also indicate that a vaccine could be available within eighteen months.
For a copy of a pre-print go to https://doi.org/10.2196/preprints.22483
Author(s): Bernardo Pereira Cabral, Luiza Braga, Fabio Batista Mota
Organization(s): Oswaldo Cruz Foundation, Federal University of Bahia
Source: pre-print Journal of Medical Internet Research
This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed.
For FULL-TEXT go to https://doi.org/10.1371/journal.pone.0236307
Author(s): Caroline V. Fry, Xiaojing Cai, Yi Zhang, Caroline S. Wagner
Organization(s): University of Hawai’i at Manoa, The Ohio State University, University of Technology Sydney
Source: PloS One
- A bibliometric analysis on agents for sludge dewatering was conducted.
- Highly cited papers support applied research.
- Japan is application-oriented and USA performed well in both fundamental research and applied research.
- Developing countries still need to optimize their pattern or promote technology innovation.
- Interdisciplinary collaboration covering the lifecycle of industrial chain is needed.
In recent years, agents for sludge dewatering have received extensive attention both from fundamental and applied research. Papers in the field of sludge dewatering agents are mainly concerned with the synthesis and characterization of agents as well as their interaction mechanisms with sludge. On the other hand, patents, which are characteristically different than papers, involve the invention and application of sludge dewatering agents. However, these researches rarely use bibliometric technology and social network analysis to provide a better understanding of the fundamental and applied researches in this specific field. This paper presents a quantitative analysis based on a total of 338 (out of 1126) core papers from Web of Science Expanded and 961 (out of 4678) core patents from Derwent Innovation to provide valuable suggestions that may promote further development in the field of sludge dewatering agents. The core point of this paper is to explore the relationship between fundamental research and applied research in the field of sludge dewatering agents through citation, characteristics of different countries/regions, collaborations of research area and organizations. Our results underline that highly cited papers support applied research by citating information among them. With respect to countries, Japan paid more attention to applied research, whereas the USA performed well both in fundamental research and applied research. China has high quantity in both papers and patents in agents for sludge dewatering. However, the papers per capita of population of China is low and the pattern still needs to optimized to improve the quality in this field. The unique development path revealed that developing countries still have a long way to go in terms of technology innovation. In addition, collaboration network of research areas has shown strong cooperation among applied researches, however they lack connection with fundamental sciences, such as physics or chemistry. Furthermore, collaboration network of organizations disclosed the necessity for upstream enterprises (agent producers) to work closely with downstream enterprises (agent users) in the sludge dewatering.
Author(s): Gengping Zhang, Qi Shi, Qiannan Li, Hongtao Wang, Heyang Yuan, Wenjing Guo, Yufei Lu
Organization(s): Tongji University
Source: Journal of Cleaner Production
- Thirteen teams strive to distinguish emerging research topics in synthetic biology.
- Analyses of ten years of article abstracts predict topics in the next two years.
- Augmenting, consolidating, embedding, and clustering text help detect emergence.
- Analyses of citation patterns and research networking also help discern emergence.
We conducted a contest to predict highly active research topics. Participants analyzed ten years of Web of Science abstract records in a target technological domain (synthetic biology) so as to indicate cutting edge sub-topics likely to be actively pursued in the following two years. We describe contest procedures and results provided by thirteen participating teams.
Contestants used various topical and other fields in the abstract records; some augmented with external data. They applied at least 19 diverse methods in deriving emerging topics predicted to be actively researched in the coming two years. Besides topical text analyses, contestants variously brought to bear both backward and forward citation analyses, and network analyses, to help identify topics apt to be highly researched in the near future. This communal exercise on forecasting near-future research activity using a wide array of text analytic and other bibliometric tools provides a stimulating resource.
Author(s): Alan L. Porter, Denise Chiavetta, Nils C. Newman
Organization(s): Search Technology, Inc.
Source: Technological Forecasting and Social Change
Efforts to involve data science in policy analysis can be traced back decades but transforming analytic findings into decisions is still far from straightforward task. Data-driven decision-making requires understanding approaches, practices, and research results from many disciplines, which makes it interesting to investigate whether data science and policy analysis are moving in parallel or whether their pathways have intersected. Our investigation, from a bibliometric perspective, is driven by a comprehensive set of research questions, and we have designed an intelligent bibliometric framework that includes a series of traditional bibliometric approaches and a novel method of charting the evolutionary pathways of scientific innovation, which is used to identify predecessor–descendant relationships in technological topics. Our investigation reveals that data science and policy analysis have intersecting lines, and it can foresee that a cross-disciplinary direction in which policy analysis interacting with data science has become an emergent area in both communities. However, equipped with advanced data analytic techniques, data scientists are moving faster and further than policy analysts. The empirical insights derived from our research should be beneficial to academic researchers and journal editors in related research communities, as well as policy-makers in research institutions and funding agencies.
Author(s): Yi Zhang, Alan L. Porter, Scott Cunningham, Denise Chiavetta, Nils Newman
Organization(s): University of Technology Sydney, Search Technology Inc., University of Strathclyde
Source: IEEE Transactions on Engineering Management
In an era of exponential technological growth, business intelligence professionals are more in need than ever of an organized patent landscape in which to conduct technology forecasting and industry positioning. However, the construction of such a system requires time and trained experts, both of which are expensive investments for such a small part of any actual analysis. A natural solution is to employ machine learning (ML), a branch of artificial intelligence that uses statistical information to find patterns and make inferences. The primary benefit of using ML is that these algorithms do not require explicit instruction. In this paper, I present an analysis of feature selection for automatic patent categorization. For a corpus of 7,309 patent applications from the World Patent Information (WPI) Test Collection (Lupu, 2019), I assign International Patent Classification (IPC) section codes using a modified Naïve Bayes classifier. I compare precision, recall, and f-measure for a variety of meta-parameter settings including data smoothing and acceptance threshold. Finally, I apply the optimized model to IPC class and group codes and compare the results of patent categorization to academic literature.
Author(s): Caitlin Cassidy
Organization(s): Search Technology
Source: World Patent Information