-
BioRxiv : the Preprint Server For... Apr 2024The intuitive manipulation of specific amino acids to alter the activity or specificity of CRISPR-Cas9 has been a topic of great interest. As a large multi-domain...
The intuitive manipulation of specific amino acids to alter the activity or specificity of CRISPR-Cas9 has been a topic of great interest. As a large multi-domain RNA-guided endonuclease, the intricate molecular crosstalk within the Cas9 protein hinges on its conformational dynamics, but a comprehensive understanding of the extent and timescale of the motions that drive its allosteric function and association with nucleic acids remains elusive. Here, we investigated the structure and multi-timescale molecular motions of the recognition (Rec) lobe of GeoCas9, a thermophilic Cas9 from Geobacillus stearothermophilus. Our results provide new atomic details about the GeoRec subdomains (GeoRec1, GeoRec2) and the full-length domain in solution. Two single-point mutants, K267E and R332A, enhanced and redistributed micro-millisecond flexibility throughout GeoRec, and NMR studies of the interaction between GeoRec and its guide RNA showed that mutations reduced this affinity and the stability of the ribonucleoprotein complex. Despite measured biophysical differences due to the mutations, DNA cleavage assays reveal only modest functional differences in on-target activity, and similar specificity. These data highlight how guide RNA interactions can be tuned in the absence of major functional losses, but also raise questions about the underlying mechanism of GeoCas9, since analogous single-point mutations have significantly impacted on- and off-target DNA editing in mesophilic S. pyogenes Cas9. A K267E/R332A double mutant did modestly enhance GeoCas9 specificity, highlighting the robust evolutionary tolerance of Cas9 and species-dependent complexity. Ultimately, this work provides an avenue by which to modulate the structure, motion, and nucleic acid interactions at the level of the Rec lobe of GeoCas9, setting the stage for future studies of GeoCas9 variants and their effect on its allosteric mechanism.
PubMed: 38746279
DOI: 10.1101/2024.04.26.591382 -
BioRxiv : the Preprint Server For... May 2024Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating...
UNLABELLED
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy.
ONE SENTENCE SUMMARY
Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
PubMed: 38746136
DOI: 10.1101/2024.05.02.592202 -
Materials (Basel, Switzerland) Apr 2024Experimental research and numerical simulations of the structural response to shock waves with pulse durations of hundreds of milliseconds, or even seconds, are...
Experimental research and numerical simulations of the structural response to shock waves with pulse durations of hundreds of milliseconds, or even seconds, are extremely challenging. This paper takes typical single-layer and sandwich cylindrical shells as the research objects. The response rules of cylindrical shells under long-duration blast loadings were studied. The results show that when the pulse duration is greater than or equal to 4~5 times the first-order period of the structure, the maximum response of the structure tends to be consistent, that is, the maximum response of the cylindrical shells with different vibration shapes shows a saturation effect as the pulse duration increases. This study established the relationship between the saturation loading time and the inherent characteristics of the structure. It was found that the saturation effect was applicable under the following conditions, including different load waveforms, elastic-plastic deformation of the structure, and the loading object being a sandwich shell. This will help transform the long-duration explosion wave problem into a finite pulse-duration shock wave problem that can be realized by both experiments and numerical simulations.
PubMed: 38730796
DOI: 10.3390/ma17091990 -
Chemical Science May 2024The accumulation and deposition of amyloid fibrils, also known as amyloidosis, in tissues and organs of patients has been found to be linked to numerous devastating...
The accumulation and deposition of amyloid fibrils, also known as amyloidosis, in tissues and organs of patients has been found to be linked to numerous devastating neurodegenerative diseases. The aggregation of proteins to form amyloid fibrils, however, is a slow pathogenic process, and is a major issue for the evaluation of the effectiveness of inhibitors in new drug discovery and screening. Here, we used microdroplet reaction technology to accelerate the amyloid fibrillation process, monitored the process to shed light on the fundamental mechanism of amyloid self-assembly, and demonstrated the value of the technology in the rapid screening of potential inhibitor drugs. Proteins in microdroplets accelerated to form fibrils in milliseconds, enabling an entire cycle of inhibitor screening for Aβ40 within 3 minutes. The technology would be of broad interest to drug discovery and therapeutic design to develop treatments for diseases associated with protein aggregation and fibrillation.
PubMed: 38725489
DOI: 10.1039/d4sc00437j -
Journal of Vision May 2024Image differences between the eyes can cause interocular discrepancies in the speed of visual processing. Millisecond-scale differences in visual processing speed can...
Image differences between the eyes can cause interocular discrepancies in the speed of visual processing. Millisecond-scale differences in visual processing speed can cause dramatic misperceptions of the depth and three-dimensional direction of moving objects. Here, we develop a monocular and binocular continuous target-tracking psychophysics paradigm that can quantify such tiny differences in visual processing speed. Human observers continuously tracked a target undergoing Brownian motion with a range of luminance levels in each eye. Suitable analyses recover the time course of the visuomotor response in each condition, the dependence of visual processing speed on luminance level, and the temporal evolution of processing differences between the eyes. Importantly, using a direct within-observer comparison, we show that continuous target-tracking and traditional forced-choice psychophysical methods provide estimates of interocular delays that agree on average to within a fraction of a millisecond. Thus, visual processing delays are preserved in the movement dynamics of the hand. Finally, we show analytically, and partially confirm experimentally, that differences between the temporal impulse response functions in the two eyes predict how lateral target motion causes misperceptions of motion in depth and associated tracking responses. Because continuous target tracking can accurately recover millisecond-scale differences in visual processing speed and has multiple advantages over traditional psychophysics, it should facilitate the study of temporal processing in the future.
Topics: Humans; Motion Perception; Psychophysics; Vision, Binocular; Photic Stimulation; Adult; Depth Perception; Male; Vision, Monocular; Female; Young Adult; Reaction Time
PubMed: 38722274
DOI: 10.1167/jov.24.5.4 -
Scientific Reports May 2024Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli,...
Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.
Topics: Animals; Action Potentials; Neurons; Models, Neurological; Vocalization, Animal; Reproducibility of Results
PubMed: 38719897
DOI: 10.1038/s41598-024-58524-7 -
BioRxiv : the Preprint Server For... Apr 2024Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action...
Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub-millisecond precision, but its behavioral relevance is uncertain. To investigate the role of this temporal coding, we optimized machine learning models to perform real-world hearing tasks with simulated cochlear input. We asked how precise auditory nerve spike timing needed to be to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing. Degrading phase locking produced task-dependent effects, revealing how the use of fine-grained temporal information reflects both ecological task demands and neural implementation constraints. The results link neural coding to perception and clarify conditions in which prostheses that fail to restore high-fidelity temporal coding could in principle restore near-normal hearing.
PubMed: 38712054
DOI: 10.1101/2024.04.21.590435 -
Environmental Science & Technology May 2024For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial...
For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano- and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation. AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/microplastics in Lake Ontario and the Saint Lawrence River, respectively. Nano-DIHM provides physicochemical properties of single particles or clusters of nano/microplastics, including size, shape, optical phase, perimeter, surface area, roughness, and edge gradient. It distinguishes nano/microplastics from mixtures of organics, inorganics, biological particles, and coated heterogeneous clusters. This technology allows 4D tracking and 3D structural and spatial study of waterborne nano/microplastics. Independent transmission electron microscopy, mass spectrometry, and nanoparticle tracking analysis validates nano-DIHM data. Complementary modeling demonstrates nano- and microplastics have significantly distinct distribution patterns in water, which affect their transport and fate, rendering nano-DIHM a powerful tool for accurate nano/microplastic life-cycle analysis and hotspot remediation.
Topics: Artificial Intelligence; Microplastics; Water Pollutants, Chemical; Environmental Monitoring; Water
PubMed: 38709668
DOI: 10.1021/acs.est.3c10408 -
Philosophical Transactions of the Royal... Jun 2024Passive acoustic monitoring (PAM) is a powerful tool for studying ecosystems. However, its effective application in tropical environments, particularly for insects,...
Passive acoustic monitoring (PAM) is a powerful tool for studying ecosystems. However, its effective application in tropical environments, particularly for insects, poses distinct challenges. Neotropical katydids produce complex species-specific calls, spanning mere milliseconds to seconds and spread across broad audible and ultrasonic frequencies. However, subtle differences in inter-pulse intervals or central frequencies are often the only discriminatory traits. These extremities, coupled with low source levels and susceptibility to masking by ambient noise, challenge species identification in PAM recordings. This study aimed to develop a deep learning-based solution to automate the recognition of 31 katydid species of interest in a biodiverse Panamanian forest with over 80 katydid species. Besides the innate challenges, our efforts were also encumbered by a limited and imbalanced initial training dataset comprising domain-mismatched recordings. To overcome these, we applied rigorous data engineering, improving input variance through controlled playback re-recordings and by employing physics-based data augmentation techniques, and tuning signal-processing, model and training parameters to produce a custom well-fit solution. Methods developed here are incorporated into Koogu, an open-source Python-based toolbox for developing deep learning-based bioacoustic analysis solutions. The parametric implementations offer a valuable resource, enhancing the capabilities of PAM for studying insects in tropical ecosystems. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
Topics: Animals; Vocalization, Animal; Acoustics; Panama; Deep Learning; Species Specificity
PubMed: 38705172
DOI: 10.1098/rstb.2023.0444 -
BMC Bioinformatics May 2024Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing...
BACKGROUND
Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue-residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.
RESULT
In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue-residue distances in tens of milliseconds and has higher precision than the best structure prediction method.
CONCLUSION
Several groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue-residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.
Topics: Proteins; Computational Biology; Databases, Protein; Protein Conformation; Algorithms; Sequence Analysis, Protein; Neural Networks, Computer
PubMed: 38704533
DOI: 10.1186/s12859-024-05771-0