The idea that adaptive evolution could be rapid and highly localized was a significant enabling condition for the emergence of ecological genetics in the second half of the 20th century. Most of what historians know about that conceptual shift and the rise of ecological genetics centers on the work of Oxford zoologist E. B. Ford and his students on polymorphism in Lepidotera, especially industrial melanism in Biston betularia. I argue that ecological genetics in Britain was not the brainchild of an infamous patriarch (Ford), but rather the outgrowth of a long tradition of pastureland research at plant breeding stations in Scotland and Wales, part of a discipline known as “genecology” or “experimental taxonomy.” Bradshaw’s investigative activities between 1948 and 1968 were an outgrowth of the specific brand of plant genecology practiced at the Welsh and Scottish Plant Breeding stations. Bradshaw generated evidence that plant populations with negligible reproductive isolation—separated by just a few meters—could diverge and adapt to contrasting environmental conditions in just a few generations. In Bradshaw’s research one can observe the crystallization of a new concept of rapid adaptive evolution, and the methodological and conceptual transformation of genecology into ecological genetics.
The first chapter revisits some of the key experiments that contributed to the development of the repression model of genetic regulation in the lac operon and concludes that the early research on gene expression and genetic regulation depict an iterative and integrative process, which was neither reductionist nor holist. In doing so, it challenges a common application of a conceptual framework in the history of biology and offers an alternative framework. The second chapter argues that the concept of emergence in the history and philosophy of biology is too ambiguous to account for the current research in post-genomic molecular biology and it is often erroneously used to argue against some reductionist theses. The third chapter investigates the use of network representations of gene expression in developmental evolution research and takes up some of the conceptual and methodological problems it has generated. The concluding comments present potential avenues for future research arising from each substantial chapter.
In sum, this dissertation argues that the epistemic practices of gene expression research are an iterative and integrative process, which produces theoretical representations of the complex interactions in gene expression as networks. Moreover, conceptualizing these interactions as networks constrains empirical research strategies by the limited number of ways in which gene expression can be controlled through general rules of network interactions. Making these strategies explicit helps to clarify how they can explain the dynamic and adaptive features of genomes.
Due to the sudden outbreak of COVID-19, communities were forced to isolate themselves in their homes and take many safety precautions. Through this isolation, people experienced a lack of social interactions on a daily basis and increased boredom. Due to the new feelings of the pandemic experience, many found themselves to be engaging significantly more with technology and social media. Doing so helped many to interact with others or spend their extra time. Since people were engaging so much more with technology, there were distinct positive and negative outcomes. Some social media use helped cope with experiencing feelings of isolation by the COVID-19 pandemic, while it may have caused feelings of anxiety for others. Engaging with others helped humanize the experience of relying on social media as a replacement for receiving human interaction on a daily basis (Zhen, 2021). College students were specifically impacted by isolation in a social manner, but were also affected in other areas such as in their academic life. Social media became a critical tool for college students in coping with the challenges of the pandemic (Zhao, 2020). This paper will explore some of the ways in which social media has helped college students cope during the COVID-19 pandemic and how it may have had more negative effects on others. This will be explored through reviewing current literature and research. Research findings will be compared to interviews conducted for the purpose of this project. Three different college students were interviewed and asked a series of questions regarding their personal experience with COVID-19, mental health, and social media. The interview responses will be reviewed according to current research to spot any similarities in findings.
The recent popularity of ChatGPT has brought into question the future of many lines of work, among them, psychotherapy. This thesis aims to determine whether or not AI chatbots should be used by undergraduates with depression as a form of mental healthcare. Because of barriers to care such as understaffed campus counseling centers, stigma, and issues of accessibility, AI chatbots could perhaps bridge the gap between this demographic and receiving help. This research includes findings from studies, meta-analyses, reports, and Reddit posts from threads documenting people’s experiences using ChatGPT as a therapist. Based on these findings, only mental health AI chatbots specifically can be considered appropriate for psychotherapeutic purposes. Certain chatbots that are designed purposefully to discuss mental health with users can provide support to undergraduates with mild to moderate symptoms of depression. AI chatbots that promise companionship should never be used as a form of mental healthcare. ChatGPT should generally be avoided as a form of mental healthcare, except to perhaps ask for referrals to resources. Non mental health-focused chatbots should be trained to respond with referrals to mental health resources and emergency services when they detect inputs related to mental health, and suicidality especially. In the future, AI chatbots could be used to notify mental health professionals of reported symptom changes in their patients, as well as pattern detectors to help individuals with depression understand fluctuations in their symptoms. AI more broadly could also be used to enhance therapist training.