Acoustic hologram optimisation using automatic differentiation.
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ABSTRACT:
SUBMITTER: Fushimi T
PROVIDER: S-EPMC8209099 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ mol-1 , respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ mol-1 for MBE2 and 24.3 kJ mol-1 for MBE3. Furthermore, when compared to three manual fragmentation schemes on a 40-protein dataset, using both MBE and Fragment Molecular Orbital techniques, QFRAGS achieves comparable or often lower MAEEs. When applied to a 10-lipoglycan/glycolipid dataset, MAEs of 7.9 and 0.3 kJ mol-1 were observed at the MBE2 and MBE3 levels, respectively.Scientific Contribution This Article presents the Quick Fragmentation via Automated Genetic Search (QFRAGS), an innovative molecular fragmentation algorithm that significantly improves upon existing molecular fragmentation approaches by specifically addressing their lack of automation and effective fragmentation quality metrics. With an evolutionary optimisation strategy, QFRAGS actively pursues high quality fragments, generating fragmentation schemes that exhibit minimal energy errors on systems with hundreds to thousands of atoms. The advent of QFRAGS represents a significant advancement in molecular fragmentation, greatly improving the accessibility and computational feasibility of accurate quantum chemistry calculations.
| S-EPMC11331744 | biostudies-literature
Project description: Not available
| S-EPMC10838438 | biostudies-literature
Project description:An automatic speech recognition approach is presented which uses articulatory features estimated by a subject-independent acoustic-to-articulatory inversion. The inversion allows estimation of articulatory features from any talker's speech acoustics using only an exemplary subject's articulatory-to-acoustic map. Results are reported on a broad class phonetic classification experiment on speech from English talkers using data from three distinct English talkers as exemplars for inversion. Results indicate that the inclusion of the articulatory information improves classification accuracy but the improvement is more significant when the speaking style of the exemplar and the talker are matched compared to when they are mismatched.
| S-EPMC3189967 | biostudies-other
Project description:BackgroundClimate change has driven shifts in breeding phenology of many amphibians, causing phenological mismatches (e.g., predator-prey interactions), and potentially population declines. Collecting data with high spatiotemporal sensitivity on hibernation emergence and breeding times can inform conservation best practices. However, monitoring the phenology of amphibians can be challenging because of their cryptic nature over much of their life cycle. Moreover, most salamanders and caecilians do not produce conspicuous breeding calls like frogs and toads do, presenting additional monitoring challenges.MethodsIn this study, we designed and evaluated the performance of an environmental DNA (eDNA) droplet digital PCR (ddPCR) assay as a non-invasive tool to assess the breeding phenology of a Western Chorus Frog population (Pseudacris maculata mitotype) in Eastern Ontario and compared eDNA detection patterns to hourly automatic acoustic monitoring. For two eDNA samples with strong PCR inhibition, we tested three methods to diminish the effect of inhibitors: diluting eDNA samples, adding bovine serum albumin to PCR reactions, and purifying eDNA using a commercial clean-up kit.ResultsWe recorded the first male calling when the focal marsh was still largely frozen. Chorus frog eDNA was detected on April 6th, 6 days after acoustic monitoring revealed this first calling male, but only 2 days after males attained higher chorus activity. eDNA signals were detected at more sampling locales within the marsh and eDNA concentrations increased as more males participated in the chorus, suggesting that eDNA may be a reasonable proxy for calling assemblage size. Internal positive control revealed strong inhibition in some samples, limiting detection probability and quantification accuracy in ddPCR. We found diluting samples was the most effective in reducing inhibition and improving eDNA quantification.ConclusionsAltogether, our results showed that eDNA ddPCR signals lagged behind male chorusing by a few days; thus, acoustic monitoring is preferable if the desire is to document the onset of male chorusing. However, eDNA may be an effective, non-invasive monitoring tool for amphibians that do not call and may provide a useful complement to automated acoustic recording. We found inhibition patterns were heterogeneous across time and space and we demonstrate that an internal positive control should always be included to assess inhibition for eDNA ddPCR signal interpretations.
| S-EPMC9879156 | biostudies-literature
Project description:BackgroundAutomated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification.MethodsA derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all-inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance.ResultsThe VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95).ConclusionCustom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.
| S-EPMC8739609 | biostudies-literature
Project description:During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes. We define two biologically-inspired protocols that prevent kinetic trapping through either internal design of subunit binding kinetics or external design of subunit titration in time. Our third protocol acts to recycle intermediates, mimicking energy-consuming enzymes. Preventative solutions via interface design are the most efficient and scale better with more subunits, but external control via titration or recycling are effective even for poorly evolved binding kinetics. Whilst all protocols can produce good solutions, diverse subunits always helps; these complexes access more efficient solutions when following external control protocols, and are simpler to design for internal control, as molecular interfaces do not need modification during assembly given sufficient variation in dimerization rates. Our results identify universal scaling in the cost of kinetic trapping, and provide multiple strategies for eliminating trapping and maximizing assembly yield across large parameter spaces.SignificanceMacromolecular complexes are frequently composed of diverse subunits. While evolution may favor repeated subunits and symmetry, we show how diversity in subunits generates an expansive parameter space that naturally improves the 'expressivity' of self-assembly, much like a deeper neural network. By using automatic differentiation algorithms commonly used in deep learning, we searched these parameter spaces to identify classes of kinetic protocols that mimic biological solutions for productive self-assembly. Our results reveal how high-yield complexes that easily become kinetically trapped in incomplete intermediates can instead be steered by internal design of rate constants or external and active control of subunits to efficiently assemble, exploiting nonequilibrium control of these ubiquitous dynamical systems.
| S-EPMC10491160 | biostudies-literature
Project description:Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.
| S-EPMC7265409 | biostudies-literature
Project description:Belugas (Delphinapterus leucas) and narwhals (Monodon monoceros) are highly social Arctic toothed whales with large vocal repertoires and similar acoustic profiles. Passive Acoustic Monitoring (PAM) that uses multiple hydrophones over large spatiotemporal scales has been a primary method to study their populations, particularly in response to rapid climate change and increasing underwater noise. This study marks the first acoustic comparison between wild belugas and narwhals from the same location and reveals that they can be acoustically differentiated and classified solely by echolocation clicks. Acoustic recordings were made in the pack ice of Baffin Bay, West Greenland, during 2013. Multivariate analyses and Random Forests classification models were applied to eighty-one single-species acoustic events comprised of numerous echolocation clicks. Results demonstrate a significant difference between species' acoustic parameters where beluga echolocation was distinguished by higher frequency content, evidenced by higher peak frequencies, center frequencies, and frequency minimums and maximums. Spectral peaks, troughs, and center frequencies for beluga clicks were generally > 60 kHz and narwhal clicks < 60 kHz with overlap between 40-60 kHz. Classification model predictive performance was strong with an overall correct classification rate of 97.5% for the best model. The most important predictors for species assignment were defined by peaks and notches in frequency spectra. Our results provide strong support for the use of echolocation in PAM efforts to differentiate belugas and narwhals acoustically.
| S-EPMC8589986 | biostudies-literature
Project description:Production of cultured meat requires defined medium formulations for the robust differentiation of myogenic cells into mature skeletal muscle fibres in vitro. Whilst such formulations can drive myogenic differentiation to an extent similar to serum-starvation based protocols, these cultures are visually heterogeneous in nature, with a significant proportion of cells not participating in myofusion, limiting the maturation of the muscle. Here, we use RNA sequencing to characterise this heterogeneity at single-nucleus resolution, identifying distinct cellular subpopulations, including proliferative cells that fail to exit the cell cycle, and 'reserve cells' that do not commit to myogenic differentiation. By targeting the ERK, RXR and NOTCH pathways, we show that cell cycle exit can be promoted whilst simultaneously abrogating reserve cell formation. Under these improved culture conditions, fusion indices close to 100% can be robustly obtained in 2D culture. We further show that this translates to higher levels of myotube formation and muscle protein accumulation in animal-free bioartificial muscle (BAM) constructs, providing proof of principle for the generation of highly differentiated cultured muscle with excellent mimicry to traditional meat.
2023-09-06 | GSE240556 | GEO
Project description:IntroductionAcoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segment some of the major fiber bundles in the brain. This study aims to train TractSeg to segment the core of acoustic radiation.MethodsWe propose a methodology to automatically extract the acoustic radiation from human connectome data, which is both of high quality and high resolution. The segmentation masks generated by TractSeg of nearby fiber bundles are used to steer the generation of valid streamlines through tractography. Only streamlines connecting the Heschl's gyrus and the medial geniculate nucleus were considered. These streamlines are then used to create masks of the core of the acoustic radiation that is used to train the neural network of TractSeg. The trained network is used to automatically segment the acoustic radiation from unseen images.ResultsThe trained neural network successfully extracted anatomically plausible masks of the core of the acoustic radiation in human connectome data. We also applied the method to a dataset of 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls acquired in a clinical setting. The method was able to extract 53/68 acoustic radiation in the dataset acquired with clinical settings. In 14/68 cases, the method generated fragments of the acoustic radiation and completely failed in a single case. The performance of the method on patients and controls was similar.DiscussionIn most cases, it is possible to segment the core of the acoustic radiations even in images acquired with clinical settings in a few seconds using a pre-trained neural network.
| S-EPMC9539320 | biostudies-literature