Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy.

Background: Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. Methods: We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Results: Mean (95% confidence interval) GBM segmentation accuracy, calculated as Jaccard index, was 0.54 (0.52-0.56) for WT and 0.61 (0.56-0.66) for ILK cKO TEM images. Automated and corresponding manual measured PFP widths differed significantly for both WT (p<0.05) and ILK cKO (p<0.05), while automated and manual GBM widths differed only for ILK cKO (p<0.05) but not WT (p=0.49) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (p<0.05) and PFP (p<0.05) width measurements. This phenotypic difference was reflected in the automated GBM (p=0.06) more than PFP (p=0.20) widths. Conclusions: These results suggest that certain automated measurements enabled via deep learning-based digital pathology tools could distinguish healthy kidneys from those with podocytopathy. Our proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research and translate into clinical diagnosis.

Brain-inspired modular echo state network for EEG-based emotion recognition.

Previous studies have successfully applied a lightweight recurrent neural network (RNN) called Echo State Network (ESN) for EEG-based emotion recognition. These studies use intrinsic plasticity (IP) and synaptic plasticity (SP) to tune the hidden reservoir layer of ESN, yet they require extra training procedures and are often computationally complex. Recent neuroscientific research reveals that the brain is modular, consisting of internally dense and externally sparse subnetworks. Furthermore, it has been proved that this modular topology facilitates information processing efficiency in both biological and artificial neural networks (ANNs). Motivated by these findings, we propose Modular Echo State Network (M-ESN), where the hidden layer of ESN is directly initialized to a more efficient modular structure. In this paper, we first describe our novel implementation method, which enables us to find the optimal module numbers, local and global connectivity. Then, the M-ESN is benchmarked on the DEAP dataset. Lastly, we explain why network modularity improves model performance. We demonstrate that modular organization leads to a more diverse distribution of node degrees, which increases network heterogeneity and subsequently improves classification accuracy. On the emotion arousal, valence, and stress/calm classification tasks, our M-ESN outperforms regular ESN by 5.44, 5.90, and 5.42%, respectively, while this difference when comparing with adaptation rules tuned ESNs are 0.77, 5.49, and 0.95%. Notably, our results are obtained using M-ESN with a much smaller reservoir size and simpler training process.

Continual Nuclei Segmentation via Prototype-Wise Relation Distillation and Contrastive Learning.

Deep learning models have achieved remarkable success in multi-type nuclei segmentation. These models are mostly trained at once with the full annotation of all types of nuclei available, while lack the ability of continually learning new classes due to the problem of catastrophic forgetting. In this paper, we study the practical and important class-incremental continual learning problem, where the model is incrementally updated to new classes without accessing to previous data. We propose a novel continual nuclei segmentation method, to avoid forgetting knowledge of old classes and facilitate the learning of new classes, by achieving feature-level knowledge distillation with prototype-wise relation distillation and contrastive learning. Concretely, prototype-wise relation distillation imposes constraints on the inter-class relation similarity, encouraging the encoder to extract similar class distribution for old classes in the feature space. Prototype-wise contrastive learning with a hard sampling strategy enhances the intra-class compactness and inter-class separability of features, improving the performance on both old and new classes. Experiments on two multi-type nuclei segmentation benchmarks, i.e., MoNuSAC and CoNSeP, demonstrate the effectiveness of our method with superior performance over many competitive methods. Codes are available at https://github.com/zzw-szu/CoNuSeg.

A novel transformer-based ECG dimensionality reduction stacked auto-encoders for arrhythmia beat detection.

BACKGROUND: Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long-distance reliance. PURPOSE: To reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto-encoders model using Transformer for ECG-based arrhythmia detection. And overcome the challenges of long-term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing. METHODS: In this paper, a Transformer-Based ECG Dimensionality Reduction Stacked Auto-encoders model is proposed for ECG-based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low-dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimize overfitting. RESULTS: The proposed method is benchmarked on the MIT-BIH Arrhythmia database. In the 10-fold cross validation of beat-based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record-based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively. CONCLUSIONS: Compared to other existing ECG-based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record-based data division method makes our approach more suitable for clinical practice.

Toxicity and risk priority ranking of polybrominated diphenyl ethers (PBDEs): A relative receptor-bound concentration approach.

Toxicity and risk priority ranking of chemicals are crucial to management and decision-making. In this work, we develop a new mechanistic ranking approach of toxicity and risk priority ranking for polybrominated diphenyl ethers (PBDEs) based on receptor-bound concentration (RBC). Based on the binding affinity constant predicted using molecular docking, internal concentration converted from human biomonitoring data via PBPK model, and the receptor concentration derived from the national center for biotechnology information (NCBI) database, the RBC of 49 PBDEs binding to 24 nuclear receptors were calculated. 1176 RBC results were successfully obtained and analyzed. High brominated PBDEs, including BDE-201, BDE-205, BDE-203, BDE-196, BDE-183, BDE-206, BDE-207, BDE-153, BDE-208, BDE-204, BDE-197, and BDE-209, exerted more potent than low brominated congeners (BDE-028, BDE-047, BDE-099, and BDE-100) at the same daily intake dose in terms of toxicity ranking. For risk ranking, with human biomonitoring serum data, the relative RBC of BDE-209 was significantly greater than that of any others. For receptor prioritization, constitutive androstane receptor (CAR), retinoid X receptor alpha (RXRA), and liver X receptor alpha (LXRA) may be the sensitive targets for PBDEs to trigger effects in the liver. In summary, high brominated PBDEs are more potent than low brominated congeners, thus, besides BDE-047 and BDE-099, BDE-209 should be priority controlled. In conclusion, this study provides a new approach for toxicity and risk ranking of groups of chemicals, which can readily be used for others.