When asked to make therapeutic antimicrobial decisions on clinical rotations, veterinary students will often hesitate. Given that there are frequently several possible options and not always a defined “correct or incorrect” choice when it comes to antimicrobial selections, we sought to offer students more opportunities to practice decision making in a continuum. We developed a series of small animal case vignettes with a continuum of pharmacologic choices. Rather than making a discrete drug selection on a multiple-choice exam, students use a slider bar tool to indicate the relative safety and/or efficacy of the drug in question; they also provide a written justification for their selection. Slider bar selections and justifications are anonymized and downloaded into a corresponding file for instructor review. Student participation engenders real time feedback for instructors, which shapes key learning goals for class sessions and addresses misconceptions. An example of the case used to modify a teaching approach in the classroom highlights how the tool provides insight into students’ mindset. The case detailed an intact male dog with clinical signs of a urinary tract infection and supportive urinalysis data. Students evaluated the safety and efficacy of five different drugs. Most of the students deemed the aminopenicillin or potentiated aminopenicillin as safe and effective for use in this patient; most missed the signalment in the case and ignored the role of the prostate. The case sharpened the accompanying class discussion on the significance of antimicrobial selection in an intact male dog with a urinary tract infection.

In the pursuit of environmental sustainability, the development of energy-efficient artificial intelligence (AI) models becomes imperative. This work presents a novel approach to bioacoustic monitoring using self-supervised learning (SSL) of bird sound representations. We employ MobileNetV3, a lightweight deep learning model designed to focus on low computational cost while maintaining high performance. Our method leverages the capability of SSL to exploit unlabeled data, significantly reducing the dependency on extensive labelled datasets that is costly. By choosing appropriate data augmentation techniques to train MobileNetV3 in a self-supervised manner, we extract informative features from the data. These features achieve robust few-shot learning capabilities, enabling accurate bird species recognition with one labeled example per class. The application of such a lightweight model not only mitigates the environmental impact associated with the training of large-scale AI models but also enhances the feasibility of deploying AI solutions in resource-constrained environments. In future work, we aim to extend the application of our method to the large bird sound collection Xeno-Canto, which contains recordings from over 10,000 species. This scalability will allow us to learn more robust features, potentially enhancing the generalizability and effectiveness of our model across a broader spectrum of bioacoustic challenges. Additionally, by employing model compression techniques such as distillation, we can further reduce the model size, enabling the development of an ultra-compact model that is well-suited for deployment on small devices.

As the world transitions towards sustainable energy sources to combat climate change, wind power has emerged as a promising renewable alternative. The growing demand for wind energy generation has significant interest in developing accurate wind speed and energy forecasting models. Reliable forecasting is crucial for optimizing the planning and operation of wind power plants, enabling the seamless integration of this clean and sustainable energy source into the grid. While numerous models have been proposed in the past to predict wind speed and energy, their performance has been hindered by the inherent non-linearity and irregularity of wind patterns. To address this challenge and facilitate the widespread adoption of sustainable wind power, this research introduces a Novel Modular Red Deer Neural System (MRDNS) designed to effectively forecast wind speed and energy. The MRDNS harnesses data from wind turbine SCADA databases, which undergoes pre-processing to eliminate training flaws. Relevant features are then extracted, reducing the complexity of the prediction process. By analysing these features, the MRDNS employs a fitness function to predict wind speed and energy with enhanced accuracy, supporting the efficient utilization of this renewable resource. The model achieved remarkable performance, boasting a prediction accuracy of 99.99% for wind power forecasting, with an MSE of 0.0017 and an RMSE of 0.0422. For wind speed forecasting, the model yielded an MSE of 0.0003 and an RMSE of 0.0174.

Sensor-based remote healthcare monitoring offers a sustainable solution for detecting adverse health events in individuals with long-term conditions, directly in their homes. Traditional anomaly detection methods in noisy, multivariate real-world data often require large labelled datasets, complex AI models, extensive hyperparameter tuning, and frequent retraining to address data drift, limiting their scalability and explainability. Inspired by the simplicity and success of negative sample-free contrastive learning in computer vision, we propose a resource-efficient, self-supervised model that adapts to noise to improve anomaly detection. Our model has outperformed similar algorithms in detecting agitation and fall events in a real-world study of dementia patients. We enhanced model transparency through a ‘spatiotemporal attention map’ that pinpoints anomalies, fostering user trust and encouraging broader adoption. Our scalable, domain-agnostic solution can be applied across diverse healthcare, industrial, and urban environments, aligning with sustainable development goals, particularly in low-resource settings.

Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on resource-constrained devices such as embedded systems. In this paper, we analyze how the performance of large-scale pre-trained audio neural networks designed for audio pattern recognition changes when deployed on a hardware such as a Raspberry Pi. We empirically study the role of CPU temperature, microphone quality and audio signal volume on performance. Our experiments reveal that the continuous CPU usage results in an increased temperature that can trigger an automated slowdown mechanism in the Raspberry Pi, impacting inference latency. The quality of a microphone, specifically with affordable devices such as the Google AIY Voice Kit, and audio signal volume, all affect the system performance. In the course of our investigation, we encounter substantial complications linked to library compatibility and the unique processor architecture requirements of the Raspberry Pi, making the process less straightforward compared to conventional computers (PCs). Our observations, while presenting challenges, pave the way for future researchers to develop more compact machine learning models, design heat-dissipative hardware, and select appropriate microphones when AI models are deployed for real-time applications on edge devices.

Convolutional neural networks (CNNs) are commonplace in
high-performing solutions to many real-world problems, such as
audio classification. CNNs have many parameters and filters,
with some having a larger impact on the performance than others.
This means that networks may contain many unnecessary filters,
increasing a CNN’s computation and memory requirements while
providing limited performance benefits. To make CNNs more
efficient, we propose a pruning framework that eliminates filters
with the highest “commonality”. We measure this commonality
using the graph-theoretic concept of centrality. We hypothesise
that a filter with a high centrality should be eliminated as it
represents commonality and can be replaced by other filters without
affecting the performance of a network much. An experimental
evaluation of the proposed framework is performed on acoustic
scene classification and audio tagging. On the DCASE 2021 Task
1A baseline network, our proposed method reduces computations
per inference by 71% with 50% fewer parameters at less than a
two percentage point drop in accuracy compared to the original
network. For large-scale CNNs such as PANNs designed for audio
tagging, our method reduces 24% computations per inference with
41% fewer parameters at a slight improvement in performance.

Cells secrete extracellular vesicles (EVs) through various biogenesis pathways, resulting in distinct molecular compositions even when originating from the same cells. Analyzing individual EVs is challenging due to the need to overcome issues such as nanoscale size, heterogeneity of EVs, and measurement accuracy. Overcoming these challenges in EV research not only advances the field of biopsy but also enables progress in the biological research of EVs. In this study, we analyze quantifying membrane proteins using a novel methodology based on single-molecule fluorescence spectroscopy. We used TIRF based fluorescence imaging or Fluorescence Correlation Spectroscopy (FCS) to observe antibodies or aptamers binding to single EVs. Our results demonstrate the quantification of various membrane proteins, including CD63 and CD81. Consequently, we develop a membrane protein quantification assay for individual EVs using single-molecule and particle fluorescence imaging spectroscopy. Through this single molecule assay, we will elucidate the distribution of membrane proteins in EVs derived from various tumor cells, potentially enabling their use in future biopsies or quantification studies.

Antibody-based therapies offer significant promise for the treatment of various diseases, including cancer and infectious agents. In cancer immunotherapy, these therapies aim to activate the immune system against tumor cells or inhibit their growth. However, the complex nature of cancer necessitates the exploration of multi-target therapeutic approaches, such as bispecific antibodies. These antibodies offer the potential to target multiple cancer types and variants, potentially overcoming resistance mechanisms. Single-domain antibodies (sdAbs) possess unique characteristics, including a compact size, high stability, and specific antigen recognition, making them ideal building blocks for bispecific antibody development. In this study, we describe the development of a readily manageable and cost-effective sdAb discovery platform utilizing rabbit immunization. Briefly, rabbits were immunized with a recombinant CD3 epsilon domain to elicit an immune response. Subsequently, antigen-specific sdAbs were isolated from the immunoglobulin heavy chain variable region. This accessible sdAb discovery platform has the potential to be applied in a variety of fields, including biological research and therapeutic development.

The development of bispecific antibodies that redirect the cytotoxic activity of CD3+ T cells against tumors is a promising immunotherapy strategy for hematological malignancies and solid tumors. The discovery and development of novel anti-CD3 antibodies are key to the efficacy and safety of the bispecific antibodies. Camelid-derived nanobodies have significant potential in bispecific antibodies as T cell engagers due to their small size, low production cost, high stability, and antigen specificity.
In this study, we identified variable nanobodies specific for the CD3 by immunizing a Vicugna pacos (alpaca) with recombinant human CD3 epsilon domain. In particular, the anti-CD3 Nb 1D10 clone showed a moderate affinity with recombinant human and Macaca fascicularis (cynomolgus monkey) CD3 and binding to CD3+ cell lines. These results highlight the potential of the nanobody for the development of nanobody-based bispecific antibodies as a T cell engager.

Cannabinoid receptor 1 (CB1R), a G protein-coupled receptor, plays a critical role in regulating appetite and exhibits increased expression in peripheral insulin-target tissues during obesity. This suggests its potential involvement in obesity-induced pro-inflammatory responses. Selective targeting of peripheral CB1R could offer a novel therapeutic approach to break the link between insulin resistance and metabolic inflammation. However, The widespread distribution of CB1R, including the central nervous system (CNS), presents a challenge. CNS-directed CB1R blockade can lead to severe psychological effects like depression and suicidality. This study investigates the development of peripherally-restricted, high-affinity single-domain antibodies (sdAbs) targeting CB1R for selective appetite modulation. We employed an APG-solubilized, recombinant CB1R for rabbit immunization. Antigen-specific sdAbs were subsequently isolated from the immunoglobulin heavy chain variable region. Biopanning of the resulting phage display library was conducted to identify sdAbs with high binding affinity for CB1R. Our findings demonstrate the development of CB1R-specific sdAbs, potentially offering a novel and targeted strategy for obesity management with minimized CNS exposure and reduced risk of associated psychiatric side effects