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.
Exocytosis is a complex process involving the regulated release of neurotransmitters from presynaptic neurons, and precise control of this process is crucial for neurotransmission. Synapsin and the SNARE (Soluble NSF Attachment Protein Receptor) complex are proteins that play significant roles in regulating exocytosis. Studies have demonstrated that synapsin modulates vesicle release by controlling the movement of vesicles to the active zone, where the SNARE complex facilitates vesicle fusion with the presynaptic membrane. Despite synapsin being the most abundant protein in neurons and both proteins interacting with synaptic vesicles, the role of synapsin in modulating SNARE dynamics remains unclear. In this investigation, we employed magnetic tweezers to probe the interaction between synapsin and the SNARE complex. By exerting controlled forces on individual SNARE complexes in the presence of synapsin, we observed that synapsin can impact the mechanical properties of SNARE, implying a potential role for synapsin in regulating neurotransmitter release through its effects on SNARE dynamics. These findings emphasize the importance of exploring synaspin-SNARE interactions in the nervous system and offer fresh insights into the role of synapsin in neuronal function.
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.
Audio-to-talking face generation stands at the forefront of advancements in generative AI. It bridges the gap between audio and visual representations by generating synchronized and realistic talking faces. This significantly improves human-computer interaction and content accessibility for diverse audiences. Despite substantial research in this area, critical challenges such as the lack of realistic facial animations, inaccurate audio-lip synchronization, and intensive computational demands continue to restrict the practicality of the talking face generation methods applications. To address these issues, we introduce a novel approach leveraging the emerging capabilities of Stable diffusion models and vision Transformers for Talking face generation (StableTalk). By incorporating the Re-attention mechanism and adversarial loss into StableTalk, we have markedly enhanced the audio-lip alignment and the consistency of facial animations across frames. More importantly, we have optimized computational efficiency by refining operations within the latent space and dynamically adjusting the visual focus based on the given conditions. Our experimental results demonstrate that StableTalk surpasses existing methods in terms of image quality, audio-lip synchronization, and computational efficiency.
The eukaryotic cell cycle, a pivotal biological process, has been extensively studied and
mathematically modelled in recent decades. Despite concerted efforts, identifying the minimal gene set essential for orderly cell cycle progression remains elusive. Synthetic biology, renowned for genetic engineering applications, also provides a pathway for addressing fundamental biological queries through “learning from building.” The Synthetic Yeast Genome (Sc2.0) project exemplifies this by synthesising Saccharomyces cerevisiae’s genome with changes that advance our understanding of eukaryotic genomes.
Expanding from Sc2.0’s groundwork, we aim to pioneer synthetic yeast genomes that are
minimal, modular, and reprogrammable. As a proof-of-concept, we constructed a synthetic
genome module housing nine of the key cell cycle genes. Employing CRISPR, we
systematically deleted these genes from their native loci and reinserted them together as a
synthetic gene cluster. While individually non-essential, the combined absence of all nine
genes renders this synthetic module indispensable.
Through Cre/loxP-mediated recombination, we investigated the gene combinations necessary for yeast cell cycle progression. Cre recombinase facilitated targeted gene deletions between intergenic loxP sites within the module, and rapidly generated diverse strains with combinatorial cluster deletion profiles, covering all potential combinations. Using flow cytometry sorting, we developed a way to isolate hundreds of viable deletion combinations and developed the Pool of Long Amplified Reads (POLAR) sequencing technique to enable the analysis of gene deletion frequency and gene content combinations for hundreds of strains with different cell cycle modules. These experimental findings were compared to computational models of the cell cycle and get us closer to understanding the minimal gene content for this function.
Upon pioneering this work, we now envisage a future where genome designers can predict
gene sets necessary for specialised tasks and can then synthetically arrange these genes on chromosomes and design intergenic regions to regulate their gene expression appropriately.
International commerce is a sphere where well-built customs rules are crucial. Nevertheless, due to existence of illegal acts and fraudulent undertakings, there is an urge for safety and economic soundness in customs controls. India’s customs service and related organizations employ artificial intelligence-based technologies that aid in combating illegal trade globally. The paper examines how AI can be used to identify people who misuse technology for illicit imports or exports. These evaluations also demonstrate how border control has become more dependent on AI, identify major concerns, and predict future trends. AI may provide an opportunity to strengthen border security as well as expedite legal business relations.
People quickly recognise human actions carried out in everyday activities. There is evidence that Minimal Recognisable Configurations (MIRCs) contain a combination of spatial and temporal visual features critical for reliable recognition. For complex activities, observers may have different descriptions varied in their semantic similarity (e.g., washing dishes vs cleaning dishes), potentially complicating the investigation of MIRCs in action recognition. Therefore, we measured the semantic consistency for 128 short videos of complex actions from the Epic-Kitchens-100 dataset (Damen et al., 2022), selected based on poor classification performance by our state-of-the-art computer vision network MOFO (Ahmadian et al., 2023). In an online experiment, participants viewed each video and identified the performed action by typing a description using 2-3 words (capturing action and object). Each video was classified by at least 30 participants (N=76 total). Semantic consistency of the responses was determined using a custom pipeline involving the sentence-BERT language model, which generated embedding vectors representing semantic properties of the responses. We then used adjusted pair-wise cosine similarities between response vectors to compute a ground truth description for each video, a response with the greatest semantic neighbourhood density (e.g., pouring oil, closing shelf). The greater the semantic neighbourhood density was for a ground truth candidate, the more semantically consistent were responses for the associated video. We uncovered 87 videos where semantic consistency confirmed their reliable recognisability, i.e. where cosine-similarity between the ground truth candidate and at least 70% of responses was above a similarity threshold of 0.65. We will use a subsample of these videos to investigate the role of MIRCs in human action recognition, e.g., gradually degrading the spatial and temporal information in videos and measuring the impact on action recognition. The derived semantic space and MIRCs will be used to revise MOFO into a more biologically consistent and better performing model.
Electrochemical potentials are essential for cellular life. For instance, cells generate and harness electrochemical gradients to drive a myriad of fundamental processes from nutrient uptake and ATP synthesis to neuronal transduction. To generate and maintain these gradients, all cellular membranes carefully regulate ionic fluxes using a broad array of transport proteins. For that reason, it is also extremely difficult to untangle specific ion transport pathways and link them to membrane potential variations in live cell studies. Conversely, synthetic membrane models, such as black lipid membranes and liposomes, are free of the structural complexity of cells and thus enable to isolate particular ion transport mechanisms and study them under tightly controlled conditions. Still, there is a lack of quantitative methods for correlating ionic fluxes to electrochemical gradient buildup in membrane models. Consequently, the use of these models as a tool for unravelling the coupling between ion transport and electrochemical gradients is limited. We developed a fluorescence-based approach for resolving the dynamic variation of membrane potential in response to ionic flux across giant unilamellar vesicles (GUVs). To gain maximal control over the size and membrane composition of these micron-sized liposomes, we developed an integrated microfluidic platform that is capable of high-throughput production and purification of monodispersed GUVs. By combining our microfluidic platform with quantitative fluorescence analysis, we determined the permeation rate of two biologically important electrolytes – protons (H+) and potassium ions (K+) – and were able to correlate their flux with electrochemical gradient accumulation across the lipid bilayer of single GUVs. Through applying similar analysis principles, we also determined the permeation rate of K+ across two archetypal ion channels, gramicidin A and outer membrane porin F (OmpF). We then showed that the translocation rate of H+ across gramicidin A is four orders of magnitude higher than that of K+ unlike in the case of OmpF where similar transport rates were evaluated for both ions.
This research represents a groundbreaking approach in plant phenotyping by harnessing 3D point clouds generated from video data. Focusing on the comprehensive characterization of plant traits, this method enhances the precision and depth of phenotypic analysis, crucial for advancements in genetics, breeding, and agricultural practices.
Advanced Video Data Capture and Processing for Detailed Segmentation
High-Fidelity Video Acquisition: Capturing detailed video footage of plants under varying environmental conditions forms the foundation of this method. The use of high-resolution cameras allows for capturing minute details crucial for accurate part segmentation.
Rigorous Preprocessing for Optimal Data Quality: Following capture, the video data undergoes meticulous preprocessing. Stabilization, noise filtering, and color correction are performed to ensure that the subsequent segmentation algorithms can accurately identify different parts of the plant.
Segmentation and 3D Point Cloud Generation: The application of state-of-the-art image processing algorithms segments the plant parts within each video frame. Subsequently, photogrammetry and depth estimation techniques create detailed 3D point clouds, effectively capturing the geometry of individual plant components.
Part Segmentation and Trait Measurement for Enhanced Phenotyping
Precise Plant Part Segmentation: This methodology enables the accurate segmentation of individual plant parts, such as leaves, stems, and flowers, within the 3D space. This precise segmentation is crucial for assessing complex plant traits and understanding plant structure in its entirety.
Comprehensive Trait Measurement: The 3D point clouds facilitate comprehensive measurements of plant traits. This includes quantifying leaf area, stem thickness, flower size, and even more subtle features like leaf venation patterns, providing a multi-dimensional view of plant phenotypic traits.
Temporal Tracking for Dynamic Trait Analysis: An integral advantage of using video data is the ability to track and measure these traits over time. This dynamic analysis allows for monitoring growth patterns, developmental changes, and responses to environmental stimuli in a way that static images cannot achieve.
Conclusion: A Breakthrough in Plant Phenotyping and Agricultural Research
This research significantly enhances the capability for detailed plant part segmentation and trait measurement, setting a new standard in plant phenotyping. The level of detail and accuracy afforded by this method offers invaluable insights for agricultural technology, plant genetics, and breeding programs. It represents a critical step forward in our ability to understand and optimize plant characteristics, with far-reaching implications for food production and ecological sustainability.