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Accurate cross-species 5mC detection for Oxford Nanopore sequencing in plants with DeepPlant
Abstract Nanopore sequencing enables comprehensive detection of 5-methylcytosine (5mC), particularly in repeat regions. However, CHH methylation detection in plants is limited by the scarcity of high-methylation positive samples, reducing generalization across species. Dorado, the only tool for plant 5mC detection on the R10.4 platform, lacks extensive species testing. Here, we develop DeepPlant, a deep learning model incorporating both Bi-LSTM and Transformer architectures, which significantly improves CHH detection accuracy and performs well for CpG and CHG motifs. We address the scarcity of methylation-positive CHH training samples through screening species with abundant high-methylation CHH sites using bisulfite-sequencing and generate datasets that cover diverse 9-mer motifs for training and testing DeepPlant. Evaluated across nine species, DeepPlant achieves high whole-genome methylation frequency correlations (0.705-0.838) with BS-seq data on CHH, improved by 23.4- 117.6% compared to Dorado. DeepPlant also demonstrates superior single-molecule accuracy and F1 score, offering strong generalization for plant epigenetics research.
Selective adsorption of CO2 in TAMOF-1 for the separation of CO2/CH4 gas mixtures
Proteins with proximal-distal asymmetries in axoneme localisation control flagellum beat frequency
Abstract The 9 + 2 microtubule-based axoneme within motile flagella is well known for its symmetry. However, examples of asymmetric structures and proteins asymmetrically positioned within the 9 + 2 axoneme architecture have been identified. These occur in multiple different organisms, particularly involving the inner or outer dynein arms. Here, we comprehensively analyse conserved proximal-distal asymmetries in the uniflagellate trypanosomatid eukaryotic parasites. Building on the genome-wide localisation screen in Trypanosoma brucei we identify conserved proteins with an analogous asymmetric localisation in the related parasite Leishmania mexicana . Using deletion mutants, we find which are necessary for normal cell swimming, flagellum beat parameters and axoneme ultrastructure. Using combinatorial endogenous fluorescent tagging and deletion, we map co-dependencies for assembly into their normal asymmetric localisation. This revealed 15 proteins, 9 known and 6 novel, with a conserved proximal or distal axoneme-specific localisation. Most are outer dynein arm associated and show that there are multiple classes of proximal-distal asymmetry – one which is dependent on the docking complex. Many of these proteins are necessary for retaining the normal frequency of the tip-to-base symmetric flagellar waveform. Our comprehensive mapping reveals unexpected contributions of proximal-specific axoneme components to the frequency of waveforms initiated distally.
Ex vivo cortical circuits learn to predict and spontaneously replay temporal patterns
Abstract It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two temporal patterns using dual-optical stimulation. After 24-h of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors as revealed by larger responses when the expected stimulus was omitted compared to when it was present. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.
A multi-modal transformer for predicting global minimum adsorption energy
Abstract The fast assessment of the global minimum adsorption energy (GMAE) between catalyst surfaces and adsorbates is crucial for large-scale catalyst screening. However, multiple adsorption sites and numerous possible adsorption configurations for each surface/adsorbate combination make it prohibitively expensive to calculate the GMAE through density functional theory (DFT). Thus, we designed a multi-modal transformer called AdsMT to rapidly predict the GMAE based on surface graphs and adsorbate feature vectors without site-binding information. The AdsMT model effectively captures the intricate relationships between adsorbates and surface atoms through the cross-attention mechanism, hence avoiding the enumeration of adsorption configurations. Three diverse benchmark datasets were introduced, providing a foundation for further research on the challenging GMAE prediction task. Our AdsMT framework demonstrates excellent performance by adopting the tailored graph encoder and transfer learning, achieving mean absolute errors of 0.09, 0.14, and 0.39 eV, respectively. Beyond GMAE prediction, AdsMT’s cross-attention scores showcase the interpretable potential to identify the most energetically favorable adsorption sites. Additionally, uncertainty quantification was integrated into our models to enhance the trustworthiness of the predictions.
Percolative sulfide core formation in oxidized planetary bodies
Whether or not to act is determined by distinct signals from motor thalamus and orbitofrontal cortex to secondary motor cortex
Bright and photostable yellow fluorescent proteins for extended imaging
Oxide-dispersion-enabled laser additive manufacturing of high-resolution copper
DNA origami signal amplification in lateral flow immunoassays
Abstract Lateral flow immunoassays (LFIAs) enable a rapid detection of analytes in a simple, paper-based test format. Despite their multiple advantages, such as low cost and ease of use, their low sensitivity compared to laboratory-based testing limits their use in e.g. many critical point-of-care applications. Here, we present a DNA origami-based signal amplification technology for LFIAs. DNA origami is used as a molecularly precise adapter to connect detection antibodies to tailored numbers of signal-generating labels. As a proof of concept, we apply the DNA origami signal amplification in a sandwich-based LFIA for the detection of cardiac troponin I (cTnI) in human serum. We show a 55-fold improvement of the assay sensitivity with 40-nm gold nanoparticle labels and an adjustable signal amplification of up to 125-fold with fluorescent dyes. The technology is compatible with a wide range of existing analytes, labels, and sample matrices, and presents a modular approach for improving the sensitivity and reliability of lateral flow testing.