STEM Fellowship Journal最新文献

{"title":"An unsupervised method for predicting photovoltaic potential in Canada","authors":"Bilal Shaikh, Abel Diress, Ria Patel","doi":"10.17975/sfj-2024-009","DOIUrl":"https://doi.org/10.17975/sfj-2024-009","url":null,"abstract":"To mitigate the effects of global climate change caused by fossil fuel emissions, Canada needs to reach net-zero emissions as soon as possible. However, for a country that relies heavily on non-renewable resources to heat homes, fuel transportation, and support industries, renewable alternatives must be reliable, efficient, and effective. One of the front-runners in sustainable energy solutions is solar power. Our team analyzed the photovoltaic (PV) potential of geographical sites across the country using data from the Canadian Weather Energy and Engineering Datasets (CWEEDS). Using k-means clustering, an unsupervised machine learning model, we placed 564 locations into 5 clusters and then predicted the PV potential for each cluster using a range of irradiance and radiation variables. Through plotting our results on scatter graphs, we concluded that the PV potential in most of Canada is much higher than the world average (4.11-6.96 kWh/m2). Furthermore, the province of Alberta—known for its tar sands and oil production—has the highest PV potential in the country. The province has the potential to become the leader in solar energy production in Canada. These findings can aid governments in optimizing their shift towards solar power. By identifying solar power as a strong alternative to fossil fuels, administrations can start working towards setting up solar farms in places where they would optimally serve Canadians in order to take the first step in decreasing our national carbon footprint.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"25 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings from the 2023 Indicium Conference","authors":"","doi":"10.17975/sfj-2024-008","DOIUrl":"https://doi.org/10.17975/sfj-2024-008","url":null,"abstract":"Indicium is an annual research competition geared towards introducing inexperienced students to the process of independent research. Through this mentorship program, undergraduate students are grouped with PhDs, professors, graduate students, medical students, etc. who are experienced in STEM research fields. Mentors actively guide and support students through a research project in their field of expertise and prepare them for the final Indicium Research Conference. The program also includes workshops focusing on various aspects of project development as well as networking opportunities within the greater scientific community. This year, Indicium was hosted at four university branches across Canada including McMaster University, York University, University of Toronto St. George, and University of Toronto Mississauga. Every branch held a university-level conference where participating teams were granted abstract publications in the National peer-reviewed STEM Fellowship Journal. Ten teams selected from these branches moved forward to participate in the National Indicium Research Conference held on August 12, 2023. Winning teams from this competition have been granted a full manuscript publication. We are pleased to be showcasing the conference proceedings from the four participating Canadian Indicium branches. Working alongside the incredible group of mentors, mentees, judging panel, and executive team on this initiative was an honour. Indicium would not be possible without the drive of those who chose to indulge in this platform in the pursuit of knowledge, mentorship, and future opportunities.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"61 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRISPR technology for Parkinson’s disease: Recent advancements and ongoing challenges","authors":"Rishima Mathur, Marissa Seamon","doi":"10.17975/sfj-2024-007","DOIUrl":"https://doi.org/10.17975/sfj-2024-007","url":null,"abstract":"Parkinson’s disease (PD) is a neurodegenerative disorder caused by decreased dopamine, resulting in impaired motor function. Various gene editing methods are used in PD research to understand the disease’s complexity and develop treatments. With no cure and limited treatments, it is important to understand the recent advances in PD research, particularly with new gene editing technologies. Therefore, we evaluated recent advancements in gene therapy and CRISPR technology in PD research, using Pubmed to identify CRISPR use in PD research conducted within the past ten years. We compiled cell and gene therapy clinical trials for PD using clinicaltrials.gov, finding no current therapies approved for PD treatment, and CRISPR has yet to be incorporated in any clinical trials. We organized CRISPR technology used in PD research into three study types: animal models, stem cells, and cell culture. The studies reviewed involve research into genetic forms of PD and pathological hallmarks, such as α-synuclein accumulation, mitochondrial dysfunction, and cell death. Double or triple-transgenic models and induced pluripotent stem cells have been utilized more recently, contributing critical information to the understanding of PD. CRISPR is a powerful tool that has significantly advanced PD research. However, much research is still required to fully unravel the pathology and see whether CRISPR can be used in therapies to correct gene mutations and improve dysfunctional mechanisms across PD patients. Overall, CRISPR techniques for use in PD treatments are still in early development, being tested using cell and animal models that will hopefully move into clinical trials soon.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"37 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of nanoparticles in the diagnosis and treatment of multiple sclerosis: A scoping review","authors":"Edson Kenzo Takei","doi":"10.17975/sfj-2024-005","DOIUrl":"https://doi.org/10.17975/sfj-2024-005","url":null,"abstract":"Multiple sclerosis (MS) is an autoimmune disease for which there is no existing cure. Diagnosis of the disease occurs primarily by analysis of demyelinated lesions, and their dissemination in space and time. Nanoparticles (NPs) are currently being investigated for diagnostic and therapeutic applications for MS due to their unique physical and chemical properties. This review aims to investigate the use of NPs for the diagnosis and treatment of CNS disorders, to investigate the applicability of NPs to assist in the diagnosis and treatment of MS. In this scoping review, 24 studies on different applications of NPs for diagnosis and treatment of MS as well as studies on their safety both in vivo and vitro were analyzed. The results indicate that the majority of studies on the different applications of NPs opted for intravenous and intraperitoneal administration routes with NP size varying from 5.6-500 nm. NPs were used for better enhancement and identification of demyelinating lesions in the central nervous system (CNS) by labelling immune cells. As for drug delivery applications, NPs were shown to increase cargo half-life, and enable the controllable release of drugs. Studies on their safety indicates that while particle size, concentration, and the target tissue greatly influence a NP’s biocompatibility, they are relatively safe for short-term use. These results indicate that NPs’ success in experimental models of demyelinating diseases should be further studied for its future application to assist in the diagnosis and treatment of patients with MS. Further analysis of long-term adverse effects, experimental models employed by different studies, use of various compounds to enhance NPs’ effect in the CNS, and the study of future use of NPs in theranostic applications are needed before clinical application can be considered.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2023-2024 High School Big Data Challenge: Leveraging Generative AI and Data Cybersecurity to Conserve and Foster Local Biodiversity","authors":"","doi":"10.17975/sfj-2024-004","DOIUrl":"https://doi.org/10.17975/sfj-2024-004","url":null,"abstract":"The STEM Fellowship High School Big Data Challenge provides students with the unique opportunity of Open Data inquiry into one of the UN Sustainable Development Goals and experiential learning of fundamentals of data analysis – an essential skill set for a young researcher in the digital age. This year, students explore Generative AI and Data Cybersecurity to Conserve and Foster Local Biodiversity and to suggest their own evidence-based solutions following the principles of Open Science. They investigated different topics, ranging from Enhancing Forest Fire Predictions with Sequential Models for Ecosystem Preservation and Public Safety to Leveraging Semantic Segmentation to Perform Wildfire Prediction. We designed an interdisciplinary and agile educational environment, and in-depth learning modules for students as a means of bridging the gap between traditional high school courseware and digital reality and computational science. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, CISCO Networking Academy, Canadian Science Publishing, Schulich Foundation, SciNet at University of Toronto, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. We were privileged to witness first-hand the analytical capabilities of the data-native generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"137 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The association of early-life household endotoxin exposure with asthma, recurrent wheeze, and allergic sensitization in Canadian children 3 years of age","authors":"Laura Wang, Jaclyn Parks, Elinor Simons, Anita L. Kozyrskyj, Piushkumar Mandhane, Allan Becker, James Scott, Diana Lefebvre, Stuart Turvey, Malcolm Sears, Jeffrey Brook, Theo J. Moraes, Padmaja Subbarao, Tim Takaro","doi":"10.17975/sfj-2023-008","DOIUrl":"https://doi.org/10.17975/sfj-2023-008","url":null,"abstract":"Asthma and allergies are the leading chronic illnesses among children in Canada, causing a significant burden on healthcare systems and negatively impacting the quality of life of children and their families. Currently, the association between asthma, wheeze, and atopy development and early-life exposure to endotoxin is not fully understood. Data from the CHILD Cohort Study were analyzed using multivariate logistic regression modelling to determine whether an association exists between household early-life endotoxin exposure measured in house dust and asthma, wheeze, and allergy development at 3 years of age. The models were adjusted for covariates relating to the child’s home environment, demographics and socioeconomic status. Those with higher household endotoxin concentrations showed lower odds of allergic sensitization at 3 years of age (OR 0.49, p=0.07 and OR 0.54, p=0.11) than those with the lowest household exposure. Sex stratification found that this relationship was specific to boys. No relationship was found between endotoxin exposure and recurrent wheeze at 3 years of age. Girls in homes with the highest exposure had lower odds of developing asthma by age 3 (p=0.10). These findings suggest endotoxin exposure in early life may protect against allergy at age 3 in Canadian children, particularly boys. Endotoxin is a measure of gram-negative bacteria but may be associated with the presence of ‘good’ microbes in the home environment as well. These findings are consistent with the hygiene hypothesis and encourage more research on early-life microbiome abundance and diversity.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging open data analytics and machine learning to improve diagnosis of diseases, patients’ care, and support: Proceedings from the 2023 Inter-University Big Data and AI Challenge","authors":"","doi":"10.17975/sfj-2023-007","DOIUrl":"https://doi.org/10.17975/sfj-2023-007","url":null,"abstract":"STEM Fellowship’s Inter-University Big Data Challenge offers a distinctive opportunity for university students globally to engage in a hands-on learning experience that combines computational thinking and Big Data exploration to seek solutions to health-related challenges at the national, regional, community, and individual levels. It serves as an innovative platform for identifying and nurturing research and development talent through the application of computational science and effective scholarly communication. Within this program, participants gain access to a diverse range of workshops focused on data analytics, programming, and science communication. Through these workshops, students acquire valuable skills in Python, R, machine learning, LaTeX, and Overleaf, enabling them to tackle complex data-driven problems. By providing these tools and fostering experiential learning, the program equips students with the necessary knowledge and expertise to contribute meaningfully to the field of Data Science and its applications in various domains, including healthcare. This year, the program participants explored the theme of “Leveraging Open Data Analytics and Machine Learning to Improve Diagnosis of Diseases, Patients’ Care and Support” and suggested a whole spectrum of original Open Data and Machine Learning based ideas and solutions. The research topics presented encompass a wide range of areas, spanning from repurposing drugs for the treatment of rare diseases and employing machine learning techniques to detect the progression of Parkinson's disease, to developing an ESG-focused governance framework aimed at enhancing patient care. Overall, we received submissions from student teams from practically all leading Canadian universities, mixed teams of students from Canada and the US, and Asian universities. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the program and wish them the best for their future academic and professional endeavors. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without generous support of our sponsors: Canadian Science Publishing, IntechOpen, JMIR Publications and adMare Community.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136059990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Non-viral Delivery Vehicles for CRISPR/Cas9 Therapies","authors":"Deborah Henry","doi":"10.17975/sfj-2023-006","DOIUrl":"https://doi.org/10.17975/sfj-2023-006","url":null,"abstract":"CRISPR/Cas9 is a gene editing tool that is rapidly replacing previous technologies, such as ZFNs and TALENS, to rectify disease-causing mutations. Oftentimes, these diseases have treatment methods that target the symptoms rather than the cause. Recent innovations in bioengineering suggest that novel gene therapies may provide a better alternative than existing treatments, by correcting the cause of the disorder directly: the genomic DNA. The excitement around gene therapy, however, is abated by the challenges in how to deliver the technology. Currently, there are two methods of delivery, viral and non-viral vectors, of which non-viral vectors are considered safer and more practical. Such non-viral carriers include gold nanoparticles, lipid nanoparticles, and polymeric carriers, of which will be the focus of this paper. This review examines the efficacy of these existing non-viral carriers through a comprehensive literature analysis. We compare the percentage of cells showing the targeted change in vivo and in vitro across the different vehicles in an attempt to understand how efficacy changes across vectors. Overall, we highlight that the optimal delivery platform is likely dependent on the disease model and target tissue. In the future, researchers can use this analysis to assess currently available designs and develop new carriers for transporting CRISPR/Cas9 to specific targets in vivo and in vitro.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129245431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing global renewable energy generation trends amid volatile economic and social environments","authors":"Ziheng Wei, Zirui Wei, J. Huang","doi":"10.17975/sfj-2023-004","DOIUrl":"https://doi.org/10.17975/sfj-2023-004","url":null,"abstract":"As renewable energy (RE) rapidly integrates into society to meet the growing demand for affordable and clean energy (SDG 7, United Nations’ Sustainable Development Goal 7), it is crucial to analyze the merits and flaws of renewable energy generation by assessing its impact on the global economy and social wellbeing. This paper performs an investigative study on the correlation and causality between renewable energy, economy, and environmental indicators. The data was gathered from diverse sources, including The Center for Climate and Energy Solutions, National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA GISS), and Our World in Data, for the temperature, carbon dioxide (CO2) emissions, and gross domestic product (GDP) data. Significant Granger p-values were obtained for RE generation’s ability to forecast CO2 emissions and temperature, while discovering a strong positive correlation between CO2 and RE generation. The findings revealed that RE has limited effects on the global economy but has considerable implications on social and ecological well-being.","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122722584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of telemedicine to assist in the diagnosis of multiple sclerosis from a clinically isolated syndrome","authors":"Edson Kenzo Takei, N. Kieran","doi":"10.17975/sfj-2023-003","DOIUrl":"https://doi.org/10.17975/sfj-2023-003","url":null,"abstract":"Multiple sclerosis (MS) is a neurological autoimmune disease that affects nearly 100,000 Canadians between the ages of 20 and 49 [1]. The disease damages myelin, a protective layer surrounding nerves, which causes irreversible damage to the central nervous system (CNS) [1]. Common symptoms for patients with MS include vision impairment, loss of coordination, and cognitive impairment. As of 2015, the life expectancy of MS patients is approximately 7 years shorter than the general population, with a cause of death due to the disease itself or related conditions such as infections [1, 2]. Despite the significant global prevalence of MS and its severity, no cure has been discovered, and instead all approved treatments merely aim to slow down disease progression [1]. As such, timely diagnosis of MS is critical to minimize the more severe symptoms early in life [3].","PeriodicalId":268438,"journal":{"name":"STEM Fellowship Journal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125887951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}