Nat Comput Sci. 2024 Nov 14. doi: 10.1038/s43588-024-00725-1. Online ahead of print.

NO ABSTRACT

PMID:39543392 | DOI:10.1038/s43588-024-00725-1

Sci Rep. 2024 Nov 14;14(1):28019. doi: 10.1038/s41598-024-79175-8.

ABSTRACT

Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer offers the advantage of a shorter culture duration, which reduces exposure to laboratory conditions, potentially enhancing embryonic development within a more conducive uterine environment and improving the likelihood of successful pregnancies. In this paper, we present a novel ResNet-GRU deep-learning model to predict blastocyst formation at 72 HPI. The model considers the time-lapse images from the incubator from Day 0 to Day 3. The model predicts blastocyst formation with a validation accuracy of 93% from the cleavage stage. The sensitivity and specificity are 0.97 and 0.77 respectively. The deep learning model presented in this paper will assist the embryologist in identifying the best embryo to transfer at Day 3, leading to improved patient outcomes and pregnancy rates in ART.

PMID:39543360 | DOI:10.1038/s41598-024-79175-8

Nat Methods. 2024 Nov 14. doi: 10.1038/s41592-024-02511-3. Online ahead of print.

ABSTRACT

The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states in vivo, and compare these with in vitro differentiation. Here we utilize a set of deep learning tools to integrate and classify multiple datasets. This allows the definition of both mouse and human embryo cell types, lineages and states, thereby maximizing the information one can garner from these precious experimental resources. Our approaches are built on recent initiatives for large-scale human organ atlases, but here we focus on material that is difficult to obtain and process, spanning early mouse and human development. Using publicly available data for these stages, we test different deep learning approaches and develop a model to classify cell types in an unbiased fashion at the same time as defining the set of genes used by the model to identify lineages, cell types and states. We used our models trained on in vivo development to classify pluripotent stem cell models for both mouse and human development, showcasing the importance of this resource as a dynamic reference for early embryogenesis.

PMID:39543284 | DOI:10.1038/s41592-024-02511-3

Sci Rep. 2024 Nov 14;14(1):28050. doi: 10.1038/s41598-024-79111-w.

ABSTRACT

Parkinsonian tremor (PT) and Essential tremor (ET) exist as upper limb tremors in clinical practice. Notably, their types of trembling share similar presentations and overlapping frequencies. To enhance objectivity and efficiency in the diagnosis of these two diseases, there is a pressing need for more objective tremor classification procedures. This study proposes a novel multimodal fusion network based on a cross-attention mechanism (MFCA-Net) to automate the classification of upper limb tremors between PTs and ETs. To this end, 140 patients with PTs and ETs were recruited, and acceleration and surface electromyography (sEMG) signals were collected from the forearm during tremor episodes. To comprehensively capture the global and local features of input signals, a multiscale convolution in MFCA-Net was designed. Furthermore, the cross-attention mechanism was applied to fuse the features of the two input signals. The results demonstrate that the final classification accuracy exceeded 97.18% when MFCA-Net was used. Compared with the single acceleration signal and single sEMG signal inputs, the recognition accuracies increased by 18.91% and 10.04%, respectively. Therefore, the proposed MFCA-Net in this study serves as an objective and potential tool for assisting clinicians in the diagnosis of PT and ET patients.

PMID:39543261 | DOI:10.1038/s41598-024-79111-w

Sci Rep. 2024 Nov 14;14(1):28059. doi: 10.1038/s41598-024-78979-y.

ABSTRACT

The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present and contrast the performance gains that can be achieved via accessing larger high-quality datasets versus the gains that can be achieved from harnessing the latest deep learning architectural and training advances. Modelling neonatal EEG is particularly affected by the lack of publicly available large datasets. It is shown that greater performance gains can be achieved from harnessing the latest deep learning advances than using a larger training dataset when adopting AUC as a metric, whereas using AUC90 or AUC-PR as metrics greater performance gains are achieved from using a larger dataset than harnessing the latest deep learning advances. In all scenarios the best performance is obtained by combining both deep learning advances and larger datasets. A novel developed architecture is presented that outperforms the current state-of-the-art model for the task of neonatal seizure detection. A novel method to fine-tune the presented model towards site-specific settings based on pseudo labelling is also outlined. The code and the weights of the model are made publicly available for benchmarking future model performances for neonatal seizure detection.

PMID:39543245 | DOI:10.1038/s41598-024-78979-y

Sci Rep. 2024 Nov 14;14(1):28063. doi: 10.1038/s41598-024-78635-5.

ABSTRACT

The rapid advancement of artificial intelligence and robot technology has spurred the proposal and innovation of a coal gangue sorting robot system (CGSRS) paradigm. The time-varying raw coal flow (TVRCF) with multi-scene and full working conditions affects the gangue queue. Configuring the CGSRS scheme correctly is combative. The field environment puts forward higher requirements for the time complexity of the CGSRS multi-task allocation strategy. Therefore, this paper proposes a scheme evaluation method of the CGSRS with time-varying multi-scenario based on deep learning. Firstly, the gangue queue data set of multi-scene and full-condition TVRCF was obtained according to the belt speed, the maximum coal flow, and the uncorrelated nonlinear changes of coal flow and gangue content. The CGSRS scheme is established based on robot number and rule combination, and the multi-task allocation strategy is adjusted to generate the labels of the gangue queue. Then, the RGB sample set is established based on the labels of the gangue queue. The CGSRS scheme evaluation model is trained based on DenseNet. Finally, the CGSRS scheme evaluation method was designed to realize the prediction of a random gangue queue. In this paper, the CGSRS scheme evaluation model, the stability of the solution, and the comparison of methods are carried out. Experimental results show that the solution of the CGSRS scheme evaluation model is accurate and stable. The time complexity is significantly reduced and very stable. The CGSRS scheme evaluation method is applied to the CGSRS multi-task allocation problem, and the stability of the solution is not affected by the data. It is significantly better than the multi-task allocation strategy. The proposed method is the first attempt to apply deep learning to a multi-task allocation problem in CGSRS.

PMID:39543191 | DOI:10.1038/s41598-024-78635-5

Sci Rep. 2024 Nov 14;14(1):28024. doi: 10.1038/s41598-024-77554-9.

ABSTRACT

Distributed denial of service (DDoS) attack is one of the most hazardous assaults in cloud computing or networking. By depleting resources, this attack renders the services unavailable to end users and leads to significant financial and reputational damage. Hence, identifying such threats is crucial to minimize revenue loss, market share, and productivity loss and enhance the brand reputation. In this study, we implemented an effective intrusion detection system using deep learning approach. The suggested framework includes three phases: Data pre-processing, Data balancing, and Classification. First, we prepare the valid data, which is helpful for further processing. Then, we balance the given pre-processed data by Conditional generative adversarial network (CGAN), and as a result, we can minimize the bias towards the majority classes. Finally, we distinguish whether the traffic is attack or benign using a stacked sparse denoising autoencoder (SSDAE) with a firefly-black widow (FA-BW) hybrid optimization algorithm. All these experiments are validated through the CICDDoS2019 dataset and compared with well-received techniques. From these findings, we observed that the proposed strategy detects DDoS attacks significantly more accurately than other approaches. Based on our findings, this study highlights the crucial role played by advanced deep learning techniques and hybrid optimization algorithms in strengthening cybersecurity against DDoS attacks.

PMID:39543174 | DOI:10.1038/s41598-024-77554-9

Nat Commun. 2024 Nov 14;15(1):9886. doi: 10.1038/s41467-024-54182-5.

ABSTRACT

Cancer is a heterogeneous disease requiring costly genetic profiling for better understanding and management. Recent advances in deep learning have enabled cost-effective predictions of genetic alterations from whole slide images (WSIs). While transformers have driven significant progress in non-medical domains, their application to WSIs lags behind due to high model complexity and limited dataset sizes. Here, we introduce SEQUOIA, a linearized transformer model that predicts cancer transcriptomic profiles from WSIs. SEQUOIA is developed using 7584 tumor samples across 16 cancer types, with its generalization capacity validated on two independent cohorts comprising 1368 tumors. Accurately predicted genes are associated with key cancer processes, including inflammatory response, cell cycles and metabolism. Further, we demonstrate the value of SEQUOIA in stratifying the risk of breast cancer recurrence and in resolving spatial gene expression at loco-regional levels. SEQUOIA hence deciphers clinically relevant information from WSIs, opening avenues for personalized cancer management.

PMID:39543087 | DOI:10.1038/s41467-024-54182-5

Methods Mol Biol. 2025;2870:341-355. doi: 10.1007/978-1-0716-4213-9_17.

ABSTRACT

Rapid advancements in cryogenic electron microscopy (cryo-EM) have revolutionized the field of structural biology by enabling the determination of complex macromolecular structures at unprecedented resolutions. When cryo-EM density maps have a resolution around 3 Å, the atomic structure can be modeled manually. However, as the resolution decreases, analyzing these density maps becomes increasingly challenging. For modeling structures in lower resolution maps, deep learning can be used to identify structural features in the maps to assist in structure modeling.Here, we present a suite of deep learning-based tools developed by our lab that enable structural biologists to work with cryo-EM maps of a wide range of resolutions. For cryo-EM maps at near-atomic resolution (5 Å or better), DeepMainmast automatically models all-atom structures by tracing the main chain from local map features of amino acids and atoms detected by deep learning; DAQ score quantifies map-model fit and indicates potential misassignments in protein models. In intermediate resolution maps (5-10 Å), Emap2sec and Emap2sec+ can accurately detect protein secondary structures and nucleic acids. These tools and more are available at our web server: https://em.kiharalab.org/ .

PMID:39543043 | DOI:10.1007/978-1-0716-4213-9_17

Methods Mol Biol. 2025;2870:1-19. doi: 10.1007/978-1-0716-4213-9_1.

ABSTRACT

The secondary structures (SSs) and supersecondary structures (SSSs) underlie the three-dimensional structure of proteins. Prediction of the SSs and SSSs from protein sequences enjoys high levels of use and finds numerous applications in the development of a broad range of other bioinformatics tools. Numerous sequence-based predictors of SS and SSS were developed and published in recent years. We survey and analyze 45 SS predictors that were released since 2018, focusing on their inputs, predictive models, scope of their prediction, and availability. We also review 32 sequence-based SSS predictors, which primarily focus on predicting coiled coils and beta-hairpins and which include five methods that were published since 2018. Substantial majority of these predictive tools rely on machine learning models, including a variety of deep neural network architectures. They also frequently use evolutionary sequence profiles. We discuss details of several modern SS and SSS predictors that are currently available to the users and which were published in higher impact venues.

PMID:39543027 | DOI:10.1007/978-1-0716-4213-9_1

Spectrochim Acta A Mol Biomol Spectrosc. 2024 Nov 10;327:125417. doi: 10.1016/j.saa.2024.125417. Online ahead of print.

ABSTRACT

Design of a sensitive, cost-effective SERS substrate is critical for probing analyte in trace concentration in real field environment. Present work reports the fabrication of an oxygen (O2) plasma treated bimetallic nanofibers as a sensitive SERS platform. In contrast to the conventional nanofiber-based SERS platform, the proposed plasma-treated bimetallic nanofibers-based SERS platform offers high sensitivity and reproducibility characteristics. On top, the use of bimetallic nanoparticles provides a synergistic effect, contributing to both electromagnetic and chemical enhancement to SERS performance and the plasma treatment contributes to the controlled exposure of the embedded nanoparticles (NPs) to the analyte thereby enhancing the overall sensitivity of the proposed technique. With standard Raman active probe molecules - 1,2-bis(4-pyridyl) ethylene (BPE) and rhodamine-6G (R6G) the limit of detection (LOD) and the limit of quantification (LOQ) of the proposed sensing platform are estimated to be 3.8 nM and 11.6 nM respectively. The enhancement factor (EF) of the designed sensing platform is calculated to be ∼108 with a maximum signal variations of 5 %. The applicability of the designed SERS substrate has been realized through detection of two antibiotics - fluconazole (FLU) and lincomycin (LIN) widely used in poultry farms. Furthermore, a deep learning model - artificial neural network (ANN) has been implemented for effective classification of the analyte molecules from a mixed sample.

PMID:39541643 | DOI:10.1016/j.saa.2024.125417

Int J Med Inform. 2024 Nov 13;193:105698. doi: 10.1016/j.ijmedinf.2024.105698. Online ahead of print.

ABSTRACT

INTRODUCTION: Ultrasound equipment provides real-time visualization of internal organs, essential for early disease detection and diagnosis. However, poor-quality ultrasound images can compromise diagnostic accuracy and increase the risk of misdiagnosis. Quality assessments are often subjective, relying on the evaluator's experience and interpretation, which can vary.

METHODS: This study introduces a two-stage deep learning framework designed to objectively assess ultrasound image quality using phantom data across three key parameters: 'Dead zone', 'Axial/lateral resolution', and 'Gray scale and dynamic range'. Stage 1 automatically extracts regions of interest for each parameter, while Stage 2 employs detection or classification models to evaluate image quality within these regions. To generate an overall equipment quality score, a logistic regression model combines the weighted results from each parameter.

RESULTS: The classification model demonstrated high performance across datasets, achieving AUC scores of 98.6% for 'Dead zone', 87.7% for 'Axial/lateral resolution', and 96.0% for 'Gray scale and dynamic range'. Further analysis using guideline-compliant images of individual devices showed AUC scores of 98.2%, 92.8%, and 100%, respectively. These findings highlight deep learning's potential for quantitative and objective assessments of ultrasound image quality. Ultimately, this framework provides a streamlined approach to quality management, enabling consistent quality control and efficient scoring-based evaluation of ultrasound equipment.

PMID:39541619 | DOI:10.1016/j.ijmedinf.2024.105698

PLoS One. 2024 Nov 14;19(11):e0313640. doi: 10.1371/journal.pone.0313640. eCollection 2024.

NO ABSTRACT

PMID:39541288 | DOI:10.1371/journal.pone.0313640

Brief Bioinform. 2024 Sep 23;25(6):bbae578. doi: 10.1093/bib/bbae578.

ABSTRACT

Spatial transcriptomics technologies enable the generation of gene expression profiles while preserving spatial context, providing the potential for in-depth understanding of spatial-specific tissue heterogeneity. Leveraging gene and spatial data effectively is fundamental to accurately identifying spatial domains in spatial transcriptomics analysis. However, many existing methods have not yet fully exploited the local neighborhood details within spatial information. To address this issue, we introduce SpaGIC, a novel graph-based deep learning framework integrating graph convolutional networks and self-supervised contrastive learning techniques. SpaGIC learns meaningful latent embeddings of spots by maximizing both edge-wise and local neighborhood-wise mutual information of graph structures, as well as minimizing the embedding distance between spatially adjacent spots. We evaluated SpaGIC on seven spatial transcriptomics datasets across various technology platforms. The experimental results demonstrated that SpaGIC consistently outperformed existing state-of-the-art methods in several tasks, such as spatial domain identification, data denoising, visualization, and trajectory inference. Additionally, SpaGIC is capable of performing joint analyses of multiple slices, further underscoring its versatility and effectiveness in spatial transcriptomics research.

PMID:39541189 | DOI:10.1093/bib/bbae578

J Phys Chem A. 2024 Nov 14. doi: 10.1021/acs.jpca.4c05389. Online ahead of print.

ABSTRACT

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

PMID:39540914 | DOI:10.1021/acs.jpca.4c05389

Infect Dis (Lond). 2024 Nov 14:1-26. doi: 10.1080/23744235.2024.2425712. Online ahead of print.

ABSTRACT

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.

PMID:39540872 | DOI:10.1080/23744235.2024.2425712

Phys Chem Chem Phys. 2024 Nov 14. doi: 10.1039/d4cp04020a. Online ahead of print.

ABSTRACT

Water isotope separation, specifically separating heavy from light water, is a technologically important problem due to the usage of heavy water in applications such as nuclear magnetic resonance, nuclear power, and spectroscopy. Separation of heavy water from light water is difficult due to very similar physical and chemical properties between the isotopes. We show that a catalytically active ultrathin membrane (e.g., a nanopore in MoS2) can enable chemical exchange processes and physicochemical mechanisms that lead to efficient separation of deuterium from hydrogen. The separation process is inherently multiscale in nature with the shorter times representing chemical exchange processes and the longer timescales representing the transport phenomena. To bridge the timescales, we employ a deep learning methodology which uses short time scale ab initio molecular dynamics data for training and extends the timescales to the classical molecular dynamics regime to demonstrate isotope separation and reveal the underlying complex physicochemical processes.

PMID:39540828 | DOI:10.1039/d4cp04020a

Radiol Cardiothorac Imaging. 2024 Dec;6(6):e230419. doi: 10.1148/ryct.230419.

ABSTRACT

Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to March 2023) included 55 healthy volunteers who underwent a noncontrast cardiac MRI examination at 1.5 T. Short-axis stack DL cine sequences of the left ventricle (LV) were performed over one (1RR), three (3RR), and six cardiac (6RR) cycles and compared with a standard cine sequence (without DL, performed over 10-12 cardiac cycles) in regard to acquisition time, subjective image quality, edge sharpness, and volumetric results. Results Total acquisition time (median) for a short-axis stack was 47 seconds for the 1RR cine, 108 seconds for 3RR cine, 184 seconds for 6RR cine, and 227 seconds for the standard sequence. Volumetric results showed no difference for the conventional cine (median LV ejection fraction [EF] 63%), 6RR cine (median LVEF, 62%), and 3RR cine (median LVEF, 61%). The 1RR cine sequence significantly underestimated EF (57%) because of a different segmentation of the papillary muscles. Subjective image quality (P = .37) and edge sharpness (P = .06) of the three-heartbeat DL cine did not differ from the reference standard, while both metrics were lower for single-heartbeat DL cine and higher for six-heartbeat DL cine. Conclusion For DL-based cine sequences, acquisition over three cardiac cycles appears to be the optimal compromise, with no evidence of differences in image quality, edge sharpness, and volumetric results, but with a greater than 50% reduced acquisition time compared with the reference sequence. Keywords: MR Imaging, Cardiac, Heart, Technical Aspects, Cardiac MRI, Deep Learning, Clinical Imaging, Accelerated Imaging Supplemental material is available for this article. © RSNA, 2024.

PMID:39540821 | DOI:10.1148/ryct.230419

Med Phys. 2024 Nov 14. doi: 10.1002/mp.17513. Online ahead of print.

ABSTRACT

BACKGROUND: Four-dimensional computed tomography (4DCT) is an es sential tool in radiation therapy. However, the 4D acquisition process may cause motion artifacts which can obscure anatomy and distort functional measurements from CT scans.

PURPOSE: We describe a deep learning algorithm to identify the location of artifacts within 4DCT images. Our method is flexible enough to handle different types of artifacts, including duplication, misalignment, truncation, and interpolation.

METHODS: We trained and validated a U-net convolutional neural network artifact detection model on more than 23 000 coronal slices extracted from 98 4DCT scans. The receiver operating characteristic (ROC) curve and precision-recall curve were used to evaluate the model's performance at identifying artifacts compared to a manually identified ground truth. The model was adjusted so that the sensitivity in identifying artifacts was equivalent to that of a human observer, as measured by computing the average ratio of artifact volume to lung volume in a given scan.

RESULTS: The model achieved a sensitivity, specificity, and precision of 0.78, 0.99, and 0.58, respectively. The ROC area-under-the-curve (AUC) was 0.99 and the precision-recall AUC was 0.73. Our model sensitivity is 8% higher than previously reported state-of-the-art artifact detection methods.

CONCLUSIONS: The model developed in this study is versatile, designed to handle duplication, misalignment, truncation, and interpolation artifacts within a single image, unlike earlier models that were designed for a single artifact type.

PMID:39540716 | DOI:10.1002/mp.17513

Bioinformatics. 2024 Nov 14:btae678. doi: 10.1093/bioinformatics/btae678. Online ahead of print.

ABSTRACT

MOTIVATION: RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the discovery of such drugs, it is essential to develop an effective computational method for predicting RNA-small molecule affinity (RSMA). Recently, deep learning based computational methods have been promising due to their powerful nonlinear modeling ability. However, the leveraging of advanced deep learning methods to mine the diverse information of RNAs, small molecules and their interaction still remains a great challenge.

RESULTS: In this study, we present DeepRSMA, an innovative cross-attention-based deep learning method for RSMA prediction. To effectively capture fine-grained features from RNA and small molecules, we developed nucleotide-level and atomic-level feature extraction modules for RNA and small molecules, respectively. Additionally, we incorporated both sequence and graph views into these modules to capture features from multiple perspectives. Moreover, a Transformer-based cross-fusion module is introduced to learn the general patterns of interactions between RNAs and small molecules. To achieve effective RSMA prediction, we integrated the RNA and small molecule representations from the feature extraction and cross-fusion modules. Our results show that DeepRSMA outperforms baseline methods in multiple test settings. The interpretability analysis and the case study on spinal muscular atrophy (SMA) demonstrate that DeepRSMA has the potential to guide RNA-targeted drug design.

AVAILABILITY: The codes and data are publicly available at https://github.com/Hhhzj-7/DeepRSMA.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39540702 | DOI:10.1093/bioinformatics/btae678