Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework

Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse, and could be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit, allows for overlapping generations and a fluctuating population size, and includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, though only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning to identify the key parameters and processes that determine the population dynamics of a complex evolutionary system.

Psychosocial effects of the COVID-19 pandemic and lockdown on university students: Understanding apprehensions through a phenomenographic approach

Limited evidence exists to help understand the experiences of university students in relation to the long-term lockdown due to the COVID-19 pandemic. For that reason, we conducted a study using a phenomenographic approach in order to understand how university students perceive COVID-19 and the associated lockdown. Data were collected from 25 students in Pakistan. They were asked to demonstrate the psychological effects of the COVID-19 pandemic and lockdown in illustrations. In addition, in-depth interviews were conducted with these students, to gain further insights into their perspectives on the psychosocial effects of the COVID-19 pandemic. The analysis revealed four interlinked directions for understanding students’ experiences. These themes were: 1) escape into peace, 2) hope for personal freedom, 3) fear of becoming a victim of COVID-19, and 4) concerns regarding education, future career, and opportunities. All four themes were analyzed and condensed into an outcome space, which further gathers the perceptions of students under one theme as “Hope for life while paradoxically living with fear”. Studying the psychological impact of the COVID-19 pandemic and lockdown on students not only highlighted their concerns, but also emphasized the importance of starting regular psychological evaluations and stress-releasing sessions, along with online education to overcome growing depression.

Transnational collaboration in building educational research capacity in a new university: An applied phenomenographic investigation

This paper reports on a phenomenographic investigation concerning the perceptions of primarily early career academics on the value and importance of educational research, and the challenges and enablers of establishing and building educational research capacity, within a new university in a developing country. The study was conducted as part of a three year project which itself is based within a longitudinal programme of collaboration between the Royal University of Bhutan (RUB) and the University of the Highlands and Islands (UHI). The paper outlines the transnational collaborations between the two universities to date, before considering key issues in the development of educational research and an educational research culture within universities that are not research intensive, and for whom educational research is a nascent or emerging area. The rationale and methodological approach for the phenomenographic investigation, which seems to be the first phenomenography to address perceptions relating to the development of educational research within the context of a new university, are then considered. The main findings resulting from the phenomenographic process of thematic analysis are subsequently presented. This includes a synthesis and articulation of the findings in the form of a phenomenographic ‘outcome space’ that pertains to the academics’ motivations and perceptions in relation to the development of educational research at RUB. The paper then articulates how the findings of the phenomenography were applied in identifying and implementing tangible interventions to support the participants to engage in conducting and disseminating educational research projects relating to dimensions of their own educational practice. The paper concludes with recommendations and lessons learned relating to the development of educational research capacity in emergent and transnational collaborative contexts.

Modeling CRISPR gene drives for suppression of invasive rodents

ABSTRACTInvasive rodent populations pose a threat to biodiversity across the globe. When confronted with these new invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse. Such systems might be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit and allows for overlapping generations and a fluctuating population size. Our model includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, but only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning for identifying the key parameters and processes that determine the population dynamics of a complex evolutionary system.

A Phenomenographic Study of Adolescents’ Conceptions of Health Information Appraisal as a Critical Component of Adolescent Health Literacy

This paper reports on a health literacy study that explored adolescents’ conceptualizations of health information appraisal as a social practice in Latvia. The study was guided by phenomenography, a qualitative research approach used to describe people’s conceptions of a particular phenomenon. A purposive, maximum variation sampling was used, and 24 adolescents were recruited to take part in the study, ranging from 13 to 16 year-olds. Semi-structured interviews were undertaken for data collection. A phenomenographic method for data analysis was performed using the guidelines proved by Sandberg. The data analysis presented seven categories of description and an outcome space representing the adolescents’ qualitatively different conceptions of health information appraisal. The implications for health education in school are discussed.

Designing and verifying a tool for diagnosing scientific misconceptions in genetics topic

The main purpose of this study was to design and verify the quality of an assessment aimed at diagnosing scientific misconceptions among students enrolled in 10th-grade biology subjects. A sample consisted of N=200 students from schools under the administration of the Office of Secondary Educational Service 31, Nakhon Ratchasima province. We employed the design-based approach, which consists of four phases, namely, construct map, item design, outcome space, and Wright map. Multidimensional Random Coefficient Multinomial Logit Model (MRCMLM) was used to evaluate the quality of the assessment tool. The assessment tool consists of two dimensions, namely, knowledge and reasoning. The assessment tool is comprised of 40 items, with 20 items tapping each of the dimensions. Findings form item analysis and modeling revealed sufficient evidence on the internal structure and validity of the instrument. It can be concluded that the unidimensional model is more appropriate than the multidimensional model for diagnosing scientific misconceptions in the genetics topic for dichotomously scored with four options in each item. The polytomous scored type should be further investigated to determine the best fit model in the items relating to evidence-based reasoning for supporting students’ answers.