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
Bispecific antibodies (BsAbs) that redirect cytotoxic T lymphocytes (CTLs) via CD3 engagement represent a promising approach in cancer immunotherapy. Here, we explore the development of VNAR-based BsAbs for improved tumor targeting. Shark-derived VNARs possess unique properties such as small size, high stability, and antigen specificity, making them ideal candidates for T cell engagement. We immunized banded houndsharks (Triakis scyllium) with the recombinant human CD3 epsilon domain to isolate VNARs that specifically recognize CD3. The isolated anti-CD3 VNARs showed moderate affinity for recombinant human CD3 and bound to CD3+ cell lines. These results suggest the potential of these VNARs for the construction of VNAR-based BsAbs as T cell engagers, providing a novel avenue for cancer immunotherapy.
The neonatal Fc receptor (FcRn) extends the serum half-life of immunoglobulins G (IgG) through a pH-dependent interaction that protects and recycles IgG during intracellular trafficking. To understand the molecular basis of the prolonged half-life observed with the Fc variant KU-1, this study employs X-ray crystallography and in silico modeling to elucidate the mode of interaction (MOI) between FcRn and KU-1. Deciphering this interaction has the potential to significantly impact the development of KU-1-based biopharmaceuticals, paving the way for enhanced therapeutic efficacy and safety profiles.
CD27, a costimulatory molecule of the TNF receptor superfamily expressed on T lymphocytes, plays a critical role in regulating T cell survival, differentiation, and effector function. Upon binding its ligand, CD70, CD27 signaling enhances T cell proliferation and differentiation into effector and memory T cells. This agonistic activity positions CD27 as a promising target for immunomodulatory cancer therapy. This study investigates the development of therapeutic agents targeting the CD27 for cancer immunotherapy. We employed a recombinant human CD27 extracellular domain to immunize an alpaca (Vicugna pacos), generating antigen-specific single-domain antibodies (nanobodies). Through biopanning using the immunized phage display library, we identified the anti-CD27 cpNb4 clone exhibiting high binding affinity for recombinant human CD27 and specific binding to cell-surface CD27 expressed on CHO-K1 cells. These findings establish cpNb4 as a promising candidate for further investigation, potentially paving the way for its integration into combination immunomodulatory cancer therapy regimens.
Cellular senescence is a permanent cell proliferation arrest compromising cell regeneration and tissue repair process which gradually leads to age-related disorders. The driving factor is considered to be senescence-associated secretory phenotype(SASP) a diverse pro-inflammatory secretory factors exerted to nearby cells resulting in normal cell aging. Targeted therapies with complex drug that can selectively modulate these cells offer promising avenues for anti-aging interventions. In this study, we developed extracellular vesicles(EVs) with specific ligands as a novel drug carrier system aimed to selectively target senescent cells. By attaching ligands on the surface of EVs, we enhanced their affinity for senescent cells over normal cells and the targeting efficiency was assessed using fluorescence-activated cell sorting(FACs). The results demonstrated a significantly higher uptake of modified EVs in senescent cells which suggest that this selective delivery can effectively serve as a precision drug carrier. Future work will focus on loading therapeutic agents that exhibit senolytic or senomorphic activities to these ligand-modified EVs aiming to selectively attenuate cellular senescence.
The accumulation of aggregates of the microtubule-binding protein Tau represents a pathological hallmark in Alzheimer’s disease (AD). While Tau is primarily recognized for its interaction with microtubules, recent findings suggest the presence of Tau clusters near the plasma membrane, potentially serving as binding partners for Axonal Initial Segment (AIS)-related membrane proteins and synaptic proteins. Additionally, during AD, pathogenic tau is known to traverse the membrane via cell-to-cell transport. Furthermore, recently our group identified lipidation as a process enabling Tau’s interaction with the membrane. However, despite tau’s hydrophilic nature, the precise mechanism through which Tau dynamics might fulfill a novel physiological function by facilitating its interaction with hydrophobic lipid membranes remains elusive. In this study, we performed single-molecule imaging with total internal reflection fluorescence microscopy (TIRF) to observe tau dynamics near the plasma membrane of differentiated PC12 cells. Indeed, expression of Tau mutant constructs with inhibited lipidation in PC12 cells resulted in increased mobility of Tau near the plasma membrane. Moreover, treatment with an inhibitor targeting lipidation produced similar effects as observed with Tau mutants, suggesting that lipidation-mediated membrane interaction slows Tau mobility. In primary hippocampal neurons, we observed colocalization of Tau with lipidation-related proteins, and the Proximity Ligation Assay (PLA) confirmed their presence within 40nm proximity. This study introduces a novel post-translational modification mechanism enabling Tau interaction with the membrane. It show that Tau exhibits distinctive dynamic characteristics in close proximity to the plasma membrane, where its interaction with membrane-associated proteins could potentially serve as a potent mechanism for spatially guiding tau towards native membrane-mediated functions.
The trans-activating CRISPR RNA (tracrRNA) is fundamental to the CRISPR/Cas9 system, forming guide RNA with crRNA. Despite its known importance in crRNA maturation and Cas9 RNP-mediated DNA cleavage, the exact function of tracrRNA scaffolds remains unclear. In this investigation, we generated five tracrRNA variants by removing specific scaffolds, including Stem loops 1, 2, and 3, and the Linker. Using a new single-molecule assay, we directly observed target binding and cleavage processes guided by Cas9 RNP. Our findings underscore the vital role of the Linker in initiating R-loops and highlight the significance of Stem loop 2 in identifying PAM-distal mismatches within target DNA. Furthermore, we explored cleavage efficiency by adding tracrRNA segments, indicating that maintaining the integrity of Stem loops 2 and 3 is crucial for potent Cas9 activity. We believe that these results deepen our understanding of Cas9 functionality and offer insights into its detailed mechanism from target binding to cleavage.
Tau, known primarily as a microtubule-binding protein, is also found in the nucleus where it binds to DNA. Recent investigations have focused on its role in stabilizing DNA and chromosomes, but the biophysical understanding of its molecular mechanisms, particularly regarding tau’s phase separation properties, remains limited. In this study, we used in vitro single-molecule assays to show that tau interacts with DNA to form co-condensates, significantly altering the mechanical properties of DNA. Our findings indicate that tau can wet the DNA strand in low-salt conditions, effectively condensing and stiffening the DNA. At high concentrations, tau also forms droplet-shaped condensates on DNA, similar to its interaction with microtubules. Notably, these condensates are mobile and may act as nucleation sites for microtubule growth. This study reveals previously unknown effects of Tau-DNA condensation and suggests that these interactions could influence microtubule dynamics during mitosis.
Extracellular vesicles (EVs) are released from cells and can be taken up by other cells to mediate communication among distant cells. The process of vesicle uptake is initiated by the docking of the vesicles onto membrane proteins on cells, but a generalizable technique for quantitatively observing these vesicle–protein interactions (VPIs) is lacking. Here, we develop a technique that measures VPIs between single vesicles and cell-surface proteins using total internal reflection fluorescence microscopy. We first describe a simple procedure that can effectively label vesicles without complex purification. Subsequently, we quantify the interaction between the labeled vesicles and target proteins either attached to a surface or embedded in a lipid bilayer. By employing cell-derived vesicles (CDVs) and intercellular adhesion molecule-1 (ICAM-1) as a model system, we determine the binding affinity of vesicles toward the ICAM-1 depending on cell types of vesicle origin. Moreover, controlling the surface density of proteins also reveals robust support from a tetraspanin protein CD9, with a critical dependence on molecular proximity. We expect that VPI imaging will be a useful tool to dissect the molecular mechanisms of vesicle uptake and to guide the development of therapeutic vesicles.
Pharmaceutical and biological researchers consistently explore questions related to protein structures and mutations to better understand virus evolution. In thermodynamics, protein structures are predicted through computational simulations, such as molecular dynamic simulation, which calculates free energy, intermediate states, mutation effects, and protein-protein interactions. However, this novel method has limitations in deciphering complex protein structures. To bridge this gap in protein understanding, machine learning and deep learning are applied to study virus evolution. Notably, escape mutations of SARS-CoV-2 have been predicted using natural language processing techniques, which interpret amino acid sequences in terms of semantic change (antigenic variant) and grammatical quality (viability/fitness). Surprisingly, training models using only amino acid sequences was sufficient to predict escape mutations without additional information on protein structure and function.
Despite the potential of natural language models to suggest possible escape mutations, there is a need to enhance the accuracy of these predictions to minimize the selection of unnecessary candidates. In this study, we evaluated and refined a novel language model by incorporating nucleotide substitutions to improve prediction accuracy. Biologically, amino acid sequences are determined by nucleotide compositions, and most mutations occur at the DNA or RNA level. Although deep learning models might indirectly learn this information from amino acid sequences, integrating direct nucleotide data into the model has resulted in more precise estimations with higher accuracy. This approach has enabled us not only to reduce unnecessary candidates for escape mutations and but also to enhance prediction of characteristic and dominant mutations.
Cellular senescence is a permanent cell proliferation arrest compromising cell regeneration and tissue repair process which gradually leads to age-related disorders. The driving factor is considered to be senescence-associated secretory phenotype(SASP) a diverse pro-inflammatory secretory factors exerted to nearby cells resulting in normal cell aging. Targeted therapies with complex drug that can selectively modulate these cells offer promising avenues for anti-aging interventions. In this study, we developed extracellular vesicles(EVs) with specific ligands as a novel drug carrier system aimed to selectively target senescent cells. By attaching ligands on the surface of EVs, we enhanced their affinity for senescent cells over normal cells and the targeting efficiency was assessed using fluorescence-activated cell sorting(FACs). The results demonstrated a significantly higher uptake of modified EVs in senescent cells which suggest that this selective delivery can effectively serve as a precision drug carrier. Future work will focus on loading therapeutic agents that exhibit senolytic or senomorphic activities to these ligand-modified EVs aiming to selectively attenuate cellular senescence.