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Librarians' Instruction to Practicing Physicians: Results from a Canadian Survey [Poster]
No full textBackground/Purpose: Continuing medical education (CME) is required for the maintenance of physician competencies, and in selecting learning opportunities physicians may choose activities that align with the CanMEDS framework. This framework outlines the roles that physicians should embody to remain effective within their profession, including ‘Scholar’. With their subject expertise and demonstrated ability to teach, health sciences librarians are poised to support physicians’ growth in this domain through instructional offerings. Yet there is a paucity of literature regarding how and what health sciences librarians in Canada are teaching practicing physicians.
Methods: A bilingual, electronic survey was distributed in May 2024 to gather data on the instructional activities of Canadian health sciences librarians who teach practicing physicians. Once the survey closed, data was cleaned and analyzed and answers to open-ended questions were reviewed for common themes.
Results: Twenty-one participants responded to the survey, with sixteen remaining for analysis after meeting the inclusion criteria. Findings revealed that most librarians have taught physicians in-hospital, instruction sessions were mostly online (and synchronous), a variety of topics have been taught to this group of learners, and more.
Discussion: While this study yielded a small sample, the results indicate that Canadian health sciences librarians are actively involved in supporting physicians in their pursuit of continuous learning. With the revised CanMEDS framework expected in 2026, these findings offer an opportunity for librarians and medical education units across Canada to reflect and collaborate as they plan their future instruction
Evaluating Data Quality of Coastal Spectrophotometric pH Measurements: Implications for Ocean Acidification and Ocean Alkalinity Enhancement Research
No full textThis work examines the challenges and implications of measuring pH and other carbon system parameters in coastal and estuarine environments, where methodological inconsistencies can introduce significant biases. Chapter 2 focuses on a comparison of at-sea spectrophotometric pH measurements from two research groups during the TReX2 cruise, highlighting issues of reproducibility and internal consistency. Chapter 3 extends this discussion to the role of pH measurements in Monitoring, Reporting, and Verification (MRV) frameworks for ocean alkalinity enhancement (OAE), using data from Bedford Basin field trials to assess measurement reliability and propose future protocols.pH, which reflects the thermodynamic balance of acid-base systems in seawater, serves as a key indicator of the interplay between acidic and basic components in marine environments. When combined with another parameter, such as TA, DIC, or pCO2 the entire inorganic carbon system can be derived. However, each parameter presents methodological challenges that may introduce random or systematic errors, which then propagate through subsequent calculations. In coastal and estuarine environments, errors can become more pronounced, as standard operating procedures (SOPs) developed for open-ocean conditions may not adequately address the complexities unique to these regions. Measuring more than two parameters enables further insight into systematic errors through the evaluation of internal consistency, where existing data products often reveal pH-dependent offsets between measured pH and pH calculated from measured TA and DIC. These offsets may arise from errors in pH measurements, TA and DIC measurements, or the equilibrium constants used in the calculations, and are therefore difficult to tease apart. Comparing measurements from different research groups can help identify the specific measurement biases responsible for these offsets; however, the lack of inter-comparison studies, particularly in field settings, hinders our understanding. This work advocates for integrating internal consistency and inter-comparison studies in field conditions, as conducting them at sea provides a realistic evaluation of reproducibility between research groups. Chapter 2 utilizes this method by comparing at-sea spectrophotometric pH (pHspec) measurements from two research groups aboard the R/V Coriolis in June 2022 in the Gulf of St. Lawrence and the Lower St. Lawrence Estuary during the Tracer Release Deep Experiment 2 (TReX2) cruise. This combined analysis of reproducibility and internal consistency highlights how even minor methodological differences can substantially affect data quality, and in turn, shape data interpretation. These impacts are particularly pronounced when estimating potential bias from unidentified, excess components of TA (TAx), expected to be non-negligible in estuarine environments, where the two groups had notably different estimates. Chapter 3 draws conclusions from the discussion of data quality in estuarine environments from Chapter 2, focusing on the potential role of pHspec in Monitoring, Reporting, and Verification (MRV) frameworks for ocean alkalinity enhancement (OAE), a proposed marine carbon dioxide removal (mCDR) strategy. It incorporates insights from OAE field trial work in the Bedford Basin, Halifax, a fjord-like estuarine system, to assess the quality of pHspec, TA, and DIC data, offering an assessment of the reliability of these measurements for interpreting potential carbon dioxide removal. This chapter also includes suggestions for a future protocol for observational components of MRV frameworks
Fighting Future Flames: Modelling Forest Fire Vulnerability in Nova Scotia, Canada
No full textEarth and Environmental Sciences Undergraduate Honours ThesisHistorically, fire has been, and continues to be, a natural driver of forest renewal and regrowth, shaping Earth's landscapes into what we see today. The complex relationship between changing climate patterns, fuel types, and human activity has contributed to an increase in the frequency of forest fires. An effective method to quantify and monitor the changes to a forest ecosystem is the use of integrated remote sensing and spatial analysis techniques. In the summer of 2023, Nova Scotia experienced their most devastating fire season with 220 fires burning 25,093 hectares of land, highlighting the growing importance of monitoring forest fire vulnerability. The goal of this study is to develop a suite of indicators that, when considered together, identify areas at potential high-risk of forest fires in Nova Scotia. Two study areas were considered: Upper Tantallon and Barrington fire locations from the summer of 2023 in Nova Scotia, Canada. An ISODATA unsupervised classification was performed to identify patterns of similar spectral characteristics among biophysical variables that was used to create a map of forest fire vulnerability using an ordinal scale. The input variables include spectral indices like Normalized Difference Vegetation Index, Normalized Difference Moisture Index, slope and proximity to human-built areas, as identified across several previous studies. The vulnerability scale was tested against a high accuracy burned area classification that was generated through band differencing Sentinel-2 derived NBR (kappa 0.905). In this validation there was a high level of agreement between burned and vulnerable areas in both the reference and the map at both locations. Therefore, a significant number of areas classified as vulnerable did burn in the resulting fire. There was low agreement between the not burned and not vulnerable areas in both the reference and the map at both locations; however, this could have been caused by fire control efforts in those areas. The results of this study will help improve future wildfire science towards forest fire prediction to increase disaster preparedness and decrease the damage of forest fires.
Keywords: Forest fire; Remote sensing; Vulnerability; Unsupervised classification; Nova Scoti
The Ovarian Cancer Tumour Microenvironment Modulates Natural Killer Cell Function
No full textNatural killer (NK) cells mediate anti-tumour responses but are inhibited in the high-grade serous carcinoma (HGSC) microenvironment by factors including adenosine (ado) and human leukocyte antigen (HLA)-E/natural killer group 2A (NKG2A) checkpoint. Using flow cytometry, I assessed how ado alters HGSC cells and NK phenotypes. I genotyped HGSC patients for NKG2A polymorphisms and tested their impact on patient outcomes and immune profiles, and I genotyped healthy donors to assess NKG2A polymorphism impact on NK function in vitro. CD16 expression defined two NK subsets with distinct responses to ado; only CD16low NK cells were suppressed by ado-treated targets. NKG2A variant 5 (V5) associated with higher NKG2A expression and stronger responses to HLA-Elow targets. Ado enhanced HLA-E/NKG2A expression and NKG2A polymorphisms dictated ado suppression, revealing a novel link between metabolic and checkpoint inhibition. These findings support dual targeting of ado metabolism and NKG2A to overcome NK suppression in the HGSC microenvironment
Pre-training and self-supervised learning for speech-based mental health assessment
No full textIn this thesis, speech-based self-supervised learning models are employed to detect depression, predict depressive symptoms, and enhance the robustness of depression assessment systems through test-time training.Major depressive disorder (MDD), commonly known as depression, is a leading cause of disability, absenteeism, and premature death. Automatic depression assessment from speech is a vital step towards improving the diagnosis and treatment of this condition. While previous research has explored conventional acoustic features for speech-based depression assessment, these methods have not yet achieved clinical-level performance, highlighting the need for further advancements. A significant challenge is the non-availability of large training datasets required to train deep learning models from scratch for automated depression assessment. To address these issues, this thesis proposes the use of self-supervised learning (SSL) models based on speech to enhance the performance of automatic depression assessment systems. The pre-training objective function of SSL models determines the types of information encoded, such as semantic, speaker, and prosodic features. I first demonstrate that combining SSL models, which capture different aspects of speech—both local and global information—leads to improved performance in detecting depression. Additionally, I show that SSL-based speech embeddings are more effective at identifying specific symptoms of depression than traditional speech features. Furthermore, I compare various SSL pre-trained models to identify which aspects of speech contribute most to the detection of different symptoms. Finally, I extend test-time training (TTT) for depression detection to improve model robustness under naturally occurring covariate (distributional) shifts. This work underscores the potential of SSL techniques in developing more accurate and resilient models for depression assessment, thereby fostering further research into automated mental health evaluation
E-waste recycling practices in research labs: evaluating the effectiveness of Dalhousie University’s E-Recycling Program in the Faculties of Science and Engineering
No full textEnvironmental Problem Solving II: The Campus as a Living Laboratory Student PapersElectronic waste, abbreviated to e-waste, refers to discarded electronics. This can include items like batteries, computers, printer cartridges, or even larger appliances like refrigerators and ovens. As the use of electronics grows, so does the amount of e-waste produced. Leading to an increasing concern. Universities produce large amounts of e-waste and a significant amount of this is due to laboratories that require the use of electronics in research-based initiatives. Each department and faculty produce different amounts of e-waste depending on their specific research. This study focussed on identifying the amount and type of e-waste that gets produced in labs at Dalhousie University within the Faculties of Science and Engineering. Dalhousie University has two existing initiatives for e-waste: Green Labs and the Dalhousie University E-waste recycling program. The purpose of this research is to determine how well Dalhousie University’s E-recycling program is being used and if it is effectively meeting the electronic disposal needs of these Faculties. Data were collected using information from the E-Recycling Program and from survey responses collected from research labs within the Faculties of Science and Engineering. Both data sets were analyzed separately to determine faculty and departmental trends and any recommendations for improvements. Our results reveal that Dalhousie University’s E-Recycling Program has higher demand within the Faculty of Science on the Studley campus compared to the Faculty of Engineering on the Sexton campus. With desktop computers being most disposed of. Results from the survey responses reported the program to be accessible, albeit not adequately meeting the needs of labs. Ultimately, more awareness, convenience and communication are needed to improve Dalhousie University’s E-Recycling Program.
Keywords: E-waste, recycling, university, digitalization, obsolescence, circular economy, Research Laborator
TOWARDS DEVELOPING STANDARDIZED PRECISION AGRICULTURE BOOM SPRAYER VIA HYBRID COMMUNICATION NETWORK FOR REAL-TIME SPOT APPLICATION
No full textThe transition of boom spraying towards spot application under precision agriculture schemes faces challenges due to the large volume of data generated by a large number of sensing and actuation devices. This research focuses on developing a universal communication network for real-time spot application, using Controller Area Network (CAN) at its core, offering the advantages of potentially error-free communication and seamless integration of machine vision systems into different boom sprayers. To handle the narrow bandwidth characteristic of CAN, a novel electronic control unit (ECU) was developed to encapsulate pest detection results into CAN data frames based on detected pest locations in images received from one machine vision system consisting of multiple cameras. The machine vision data were transmitted through UART to identify the number of nozzles to be actuated via CAN. The ECU was designed to accommodate different machine vision systems with varying camera counts and image resolutions. For real-time control, the ECU extracted data every 40 ms and constructed CAN frames in two separate threads simultaneously. Field tests demonstrated that the ECU managed nozzle actuation for targets distributed across diverse scenarios, including spatial and temporal successions.
Since the conditions on wide boom sprayers require multiple machine vision systems to actuate dozens of nozzles, an upgraded communication protocol was built at the interface of the machine vision with the ECU based on Ethernet. An application layer based on ISO 11783 was added to the CAN interface, widely used in agricultural machinery including sprayers. These upgrades allowed handling nozzle actuation at variable sprayer speeds up to 9.66 kph with a minimum spray length of 345 mm per detection, processing over 30 data frames every 40 ms. Finally, a new ISO 11783-compliant CAN bus with 60 nozzles was installed on a 36 m boom sprayer, used as a case study. This new bus featured two additional ECUs: one to communicate with other buses in the sprayer to import data like speed, and another to store pest detection and nozzle actuation data for further analysis. The case study demonstrated that a complete real-time spot application mechanism, including 30 cameras, would require an additional 4034 W for full functionality
“I harboured in my belly a destructive energy”: Motherhood, Embodiment, and Creativity in Elena Ferrante’s The Lost Daughter and The Days of Abandonment
No full textReveries of a Family History Muting Present Identity An Analysis of Silence in Jimmy Corrigan: The Smartest Boy on Earth
No full textThis paper utilizes the term Ryan Twomey coined as "the sequential absence of sound" to analyze the use of silence and how it is represented in various works by Chris Ware. This paper expands Twomey's discussion by solely focusing on Jimmy Corrigan: The Smartest Boy on Earth and discerns how the sequential absence of sound occurs before, after, and during accounts of the protagonist's and his sister Amy's traumatic family history. Through Twomey's conceptual lens, this paper can identify specific moments in how Amy and Jimmy's identities have been overwritten by a history of racism as seen in the lineage of men who preceded them and how, without access to this family history, they are ill-equipped to relate to each other and move in a positive direction regarding their identities in their present moments. The use of sequential absence of sound thus accentuates these gaps in memory concerning the family's past and the inability to properly cope with the present moment.This paper analyses how silence, and how it is represented in Jimmy Corrigan: The Smartest Boy on Earth, corresponds to the development of identity for protagonist Jimmy and his sister Amy, respectively, given their family's traumatic history