Recruitment, Retention, and Training of Citizen Scientists in Translational Medicine Research: A Citizen Science Initiative on Non-Alcoholic Fatty Liver Disease

Citizen science is a participatory science approach in which members of the public (citizens) collaborate with scientists and professional researchers and become involved in research and innovation activities, resulting in the co-creation of scientific knowledge and innovation. Citizen science has been widely applied in research, particularly in the social sciences, environmental sciences, information and communication technologies, and public health. However, the application of this approach in clinical sciences, particularly in translational medicine research, is still nascent. This exploratory study involved members of the public (citizen scientists) in a translational medicine experiment on non-alcoholic fatty liver disease that incorporated a lifestyle and weight-loss intervention. The aim of this paper is to report successful methods and approaches for the recruitment, retention, and training of citizen scientists. For the citizen scientists' recruitment, online calls placed on the websites of our research project and biomedical research center and targeted emails were the most helpful. Of the 14 members of the public who expressed interest in our study, six were recruited as citizen scientists. Citizen scientists were mostly female (n = 5, 83%), white (n = 3, 50%), over 50 years of age (n = 4, 67%), educated to postgraduate level (n = 5, 83%), and either retired or not in employment (n = 5, 83%). The retention rate was 83% (n = 5), and the dropout rate was 17% (n = 1). We arranged instructor-led interactive online training sessions (an hour-long one-on-one session and two-hour group sessions). Research skills training covered ethics in research and qualitative and quantitative data analysis. Citizen scientists were given several incentives, such as reimbursement of travel and care costs, selection as citizen scientists of the month, publications of their blogs and perspective articles, and co-authorship and acknowledgement in papers and project deliverables. To conclude, members of the public (particularly middle-aged white women with postgraduate education) are interested in becoming citizen scientists in translational medicine research. Their retention rate is higher, and they can contribute to different research activities. However, they need training to develop their research skills and expertise. The training should be simple, comprehensive, and flexible to accommodate the schedules of individual citizen scientists. They deserve incentives as they work on a voluntary basis.

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation.

Revision of the genus Furusawaia Chûjô, 1962 (Coleoptera, Chrysomelidae, Galerucinae)

Yunnaniata Lopatin, 2009 is regarded as a junior synonym of Furusawaia Chûjô, 1962 syn. nov. Yunnaniatakonstantinovi Lopatin, 2009 comb. nov. is transferred to the genus Furusawaia Chûjô and redescribed. Furusawaiacontinentalis Lopatin, 2008 and F.yosonis Chûjô are recognized as valid species and redescribed. Four new species are described from Taiwan: F.jungchani sp. nov., F.lui sp. nov., F.tahsiangi sp. nov., and F.tsoui sp. nov. A key to Taiwanese and Chinese species of Furusawaia is provided.

Engaging African American Men as Citizen Scientists to Validate a Prostate Cancer Biomarker: Work-in-Progress

BACKGROUND

African American men (AAM) are under-represented in prostate cancer (PCa) research despite known disparities. Screening with prostate-specific antigen (PSA) has low specificity for high-grade PCa leading to PCa over diagnosis. The Prostate Health Index (PHI) has higher specificity for lethal PCa but needs validation in AAM. Engaging AAM as citizen scientists (CSs) may improve participation of AAM in PCa research.Results and Lessons Learned: Eight CSs completed all training modules and 139 AAM were recruited. Challenges included equity in research leadership among multiple principal investigators (PIs) and coordinating CSs trainings.

CONCLUSIONS

Engaging AAM CSs can support engaging/recruiting AAM in PCa biomarker validation research. Equity among multiple stakeholders can be challenging, but proves beneficial in engaging AAM in research.

OBJECTIVES

Assess feasibility of mobilizing CSs to recruit AAM as controls for PHI PCa validation biomarker study.

METHODS

We highlight social networks/assets of stakeholders, CSs curriculum development/implementation, and recruitment of healthy controls for PHI validation.

The application of crowdsourcing approaches to cancer research: a systematic review

Crowdsourcing is "the practice of obtaining participants, services, ideas, or content by soliciting contributions from a large group of people, especially via the Internet." (Ranard et al. J. Gen. Intern. Med. 29:187, 2014) Although crowdsourcing has been adopted in healthcare research and its potential for analyzing large datasets and obtaining rapid feedback has recently been recognized, no systematic reviews of crowdsourcing in cancer research have been conducted. Therefore, we sought to identify applications of and explore potential uses for crowdsourcing in cancer research. We conducted a systematic review of articles published between January 2005 and June 2016 on crowdsourcing in cancer research, using PubMed, CINAHL, Scopus, PsychINFO, and Embase. Data from the 12 identified articles were summarized but not combined statistically. The studies addressed a range of cancers (e.g., breast, skin, gynecologic, colorectal, prostate). Eleven studies collected data on the Internet using web-based platforms; one recruited participants in a shopping mall using paper-and-pen data collection. Four studies used Amazon Mechanical Turk for recruiting and/or data collection. Study objectives comprised categorizing biopsy images (n = 6), assessing cancer knowledge (n = 3), refining a decision support system (n = 1), standardizing survivorship care-planning (n = 1), and designing a clinical trial (n = 1). Although one study demonstrated that "the wisdom of the crowd" (NCI Budget Fact Book, 2017) could not replace trained experts, five studies suggest that distributed human intelligence could approximate or support the work of trained experts. Despite limitations, crowdsourcing has the potential to improve the quality and speed of research while reducing costs. Longitudinal studies should confirm and refine these findings.