Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex.

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Building on recent advancements in ultra-high-resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 $\mu $m, we propose a Multi-resolution U-Nets framework that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography.

Recent biotechnological developments in cryo-electron tomography allow direct visualization of native sub-cellular structures with unprecedented details and provide essential information on protein functions/dysfunctions. Denoising can enhance the visualization of protein structures and distributions. Automatic annotation via data simulation can ameliorate the time-consuming manual labeling of large-scale datasets. Here, we combine the two major cryo-ET tasks together in DUAL, by a specific cyclic generative adversarial network with novel noise disentanglement. This enables end-to-end unsupervised learning that requires no labeled data for training. The denoising branch outperforms existing works and substantially improves downstream particle picking accuracy on benchmark datasets. The simulation branch provides learning-based cryo-ET simulation for the first time and generates synthetic tomograms indistinguishable from experimental ones. Through comprehensive evaluations, we showcase the effectiveness of DUAL in detecting macromolecular complexes across a wide range of molecular weights in experimental datasets. The versatility of DUAL is expected to empower cryo-ET researchers by improving visual interpretability, enhancing structural detection accuracy, expediting annotation processes, facilitating cross-domain model adaptability, and compensating for missing wedge artifacts. Our work represents a significant advancement in the unsupervised mining of protein structures in cryo-ET, offering a multifaceted tool that facilitates cryo-ET research.

Improvement of the functional properties of insoluble dietary fiber from corn bran by ultrasonic-microwave synergistic modification.

A comprehensive investigation aimed to access the impacts of ultrasonic, microwave, and ultrasonic-microwave synergistic modification on the physicochemical properties, microstructure, and functional properties of corn bran insoluble dietary fiber (CBIDF). Our findings revealed that CBIDF presented a porous structure with loose folds, and the particle size and relative crystallinity were slightly decreased after modification. The CBIDF, which was modified by ultrasound-microwave synergistic treatment, exhibited remarkable benefits in terms of its adsorption capacity, and cholate adsorption capacity. Furthermore, the modification improved the in vitro hypoglycemic activity of the CBIDF by enhancing glucose absorption, retarding the starch hydrolysis, and facilitating the diffusion of glucose solution. The findings from the in vitro probiotic activity indicate that ultrasound-microwave synergistic modification also enhances the growth-promoting ability and adsorbability of Lactobacillus acidophilus and Bifidobacterium longum. Additionally, the level of soluble dietary fiber was found to be positively correlated with CBIDF adsorbability, while the crystallinity of CBIDF showed a negative correlation with α-glucosidase and α-amylase inhibition activity, as well as water-holding capacity, and oil-holding capacity.

Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex.

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 µm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

A widely applicable and robust LightGBM - Artificial neural network forecasting model for short-term wind power density.

Wind energy is a clean and renewable source that reduces greenhouse gas emissions. To smooth the impact of wind energy fluctuations on the power grid and power supply, much research has predicted the wind speed of wind farms to estimate power generation. However, most studies overlook the nonlinear relationship between wind speed and power generation, and data sources are usually limited to one or two wind farms. This study constructs a wind power density prediction model based on the LightGBM and artificial neural network to solve the above problems. Its data collection process does not require meteorological measurement equipment and has good universality, stability, and robustness. LightGBM is used to extract feature information and then train it using an artificial neural network, which has a high tolerance for data loss. The model performance was validated using data from six terrains from 2020 to 2022. While results showed that the average prediction error was 71.68 % less than 2 % and 82.188 % less than 6 %, with an average R2 of 0.9755 and an average correlation coefficient of 0.9875, proving the practical significance of the model that can be used to guide electricity trade.

Nanoscale details of mitochondrial constriction revealed by cryoelectron tomography.

Mitochondria adapt to changing cellular environments, stress stimuli, and metabolic demands through dramatic morphological remodeling of their shape, and thus function. Such mitochondrial dynamics is often dependent on cytoskeletal filament interactions. However, the precise organization of these filamentous assemblies remains speculative. Here, we apply cryogenic electron tomography to directly image the nanoscale architecture of the cytoskeletal-membrane interactions involved in mitochondrial dynamics in response to damage. We induced mitochondrial damage via membrane depolarization, a cellular stress associated with mitochondrial fragmentation and mitophagy. We find that, in response to acute membrane depolarization, mammalian mitochondria predominantly organize into tubular morphology that abundantly displays constrictions. We observe long bundles of both unbranched actin and septin filaments enriched at these constrictions. We also observed septin-microtubule interactions at these sites and elsewhere, suggesting that these two filaments guide each other in the cytosolic space. Together, our results provide empirical parameters for the architecture of mitochondrial constriction factors to validate/refine existing models and inform the development of new ones.

High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.

Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual labels. Here, we introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation on five experimental cryo-ET datasets shows that an unsupervised deep learning based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection paves the way for systematic unbiased recognition of macromolecular complexes in situ.

Transcriptomic congruence analysis for evaluating model organisms.

Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism's resemblance to humans in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of "poorly" or "greatly" mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions.

Apollo: Adaptive Polar Lattice-Based Local Obstacle Avoidance and Motion Planning for Automated Vehicles.

The motion planning module is the core module of the automated vehicle software system, which plays a key role in connecting its preceding element, i.e., the sensing module, and its following element, i.e., the control module. The design of an adaptive polar lattice-based local obstacle avoidance (APOLLO) algorithm proposed in this paper takes full account of the characteristics of the vehicle's sensing and control systems. The core of our approach mainly consists of three phases, i.e., the adaptive polar lattice-based local search space design, the collision-free path generation and the path smoothing. By adjusting a few parameters, the algorithm can be adapted to different driving environments and different kinds of vehicle chassis. Simulations show that the proposed method owns strong environmental adaptability and low computation complexity.

Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations.

In many real-life image analysis applications, particularly in biomedical research domains, the objects of interest undergo multiple transformations that alters their visual properties while keeping the semantic content unchanged. Disentangling images into semantic content factors and transformations can provide significant benefits into many domain-specific image analysis tasks. To this end, we propose a generic unsupervised framework, Harmony, that simultaneously and explicitly disentangles semantic content from multiple parameterized transformations. Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations. To demonstrate the efficacy of Harmony, we apply it to disentangle image semantic content from several parameterized transformations (rotation, translation, scaling, and contrast). Harmony achieves significantly improved disentanglement over the baseline models on several image datasets of diverse domains. With such disentanglement, Harmony is demonstrated to incentivize bioimage analysis research by modeling structural heterogeneity of macromolecules from cryo-ET images and learning transformation-invariant representations of protein particles from single-particle cryo-EM images. Harmony also performs very well in disentangling content from 3D transformations and can perform coarse and fast alignment of 3D cryo-ET subtomograms. Therefore, Harmony is generalizable to many other imaging domains and can potentially be extended to domains beyond imaging as well.

Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine.

The influx of hospital patients has become common in recent years. Hospital management departments need to redeploy healthcare resources to meet the massive medical needs of patients. In this process, the hospital length of stay (LOS) of different patients is a crucial reference to the management department. Therefore, building a model to predict LOS is of great significance. Five machine learning (ML) algorithms named Lasso regression (LR), ridge regression (RR), random forest regression (RFR), light gradient boosting machine (LightGBM), and extreme gradient boosting regression (XGBR) and six feature encoding methods named label encoding, count encoding, one-hot encoding, target encoding, leave-one-out encoding, and the proposed encoding method are used to construct the regression prediction model. The Scikit-Learn toolbox on the Python platform builds the prediction model. The input is the dataset named Hospital Inpatient Discharges (SPARCS De-Identified) 2017 with 2343569 instances provided by the New York State Department of Health verify the model after removing 2.2% of the missing data, and the model ultimately uses mean squared error (MSE) and coefficient of determination (R 2) as the performance measurement. The results show that the model with the LightGBM algorithm and the proposed encoding method has the best R 2 (96.0%) and MSE score (2.231).

Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms.

Macromolecular structure classification from cryo-electron tomography (cryo-ET) data is important for understanding macro-molecular dynamics. It has a wide range of applications and is essential in enhancing our knowledge of the sub-cellular environment. However, a major limitation has been insufficient labelled cryo-ET data. In this work, we use Contrastive Self-supervised Learning (CSSL) to improve the previous approaches for macromolecular structure classification from cryo-ET data with limited labels. We first pretrain an encoder with unlabelled data using CSSL and then fine-tune the pretrained weights on the downstream classification task. To this end, we design a cryo-ET domain-specific data-augmentation pipeline. The benefit of augmenting cryo-ET datasets is most prominent when the original dataset is limited in size. Overall, extensive experiments performed on real and simulated cryo-ET data in the semi-supervised learning setting demonstrate the effectiveness of our approach in macromolecular labeling and classification.

FSCC: Few-Shot Learning for Macromolecule Classification Based on Contrastive Learning and Distribution Calibration in Cryo-Electron Tomography.

Cryo-electron tomography (Cryo-ET) is an emerging technology for three-dimensional (3D) visualization of macromolecular structures in the near-native state. To recover structures of macromolecules, millions of diverse macromolecules captured in tomograms should be accurately classified into structurally homogeneous subsets. Although existing supervised deep learning-based methods have improved classification accuracy, such trained models have limited ability to classify novel macromolecules that are unseen in the training stage. To adapt the trained model to the macromolecule classification of a novel class, massive labeled macromolecules of the novel class are needed. However, data labeling is very time-consuming and labor-intensive. In this work, we propose a novel few-shot learning method for the classification of novel macromolecules (named FSCC). A two-stage training strategy is designed in FSCC to enhance the generalization ability of the model to novel macromolecules. First, FSCC uses contrastive learning to pre-train the model on a sufficient number of labeled macromolecules. Second, FSCC uses distribution calibration to re-train the classifier, enabling the model to classify macromolecules of novel classes (unseen class in the pre-training). Distribution calibration transfers learned knowledge in the pre-training stage to novel macromolecules with limited labeled macromolecules of novel class. Experiments were performed on both synthetic and real datasets. On the synthetic datasets, compared with the state-of-the-art (SOTA) method based on supervised deep learning, FSCC achieves competitive performance. To achieve such performance, FSCC only needs five labeled macromolecules per novel class. However, the SOTA method needs 1100 ∼ 1500 labeled macromolecules per novel class. On the real datasets, FSCC improves the accuracy by 5% ∼ 16% when compared to the baseline model. These demonstrate good generalization ability of contrastive learning and calibration distribution to classify novel macromolecules with very few labeled macromolecules.

Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series.

The revolutionary technique cryoelectron tomography (cryo-ET) enables imaging of cellular structure and organization in a near-native environment at submolecular resolution, which is vital to subsequent data analysis and modeling. The conventional structure detection process first reconstructs the three-dimensional (3D) tomogram from a series of two-dimensional (2D) projections and then directly detects subcellular components found within the tomogram. However, this process is challenging due to potential structural information loss during the tomographic reconstruction and the limited scope of existing methods since most major state-of-the-art object detection methods are designed for 2D rather than 3D images. Therefore, in this article, as an alternative approach to complement the conventional process, we propose a novel 2D-to-3D framework that detects structures within 2D projection images before reconstructing the results back to 3D. We implemented the proposed framework as three specific algorithms for three individual tasks: semantic segmentation, edge detection, and object localization. As experimental validation of the 2D-to-3D framework for cryo-ET data, we applied the algorithms to the segmentation of mitochondrial calcium phosphate granules, detection of spherical edges, and localization of mitochondria. Quantitative and qualitative results show better performance for prediction tasks of segmentation on the 2D projections and promising performance on object localization and edge detection, paving the way for future studies in the exploration of cryo-ET for in situ structural biology.

CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation.

Cryo-electron tomography (Cryo-ET) has been regarded as a revolution in structural biology and can reveal molecular sociology. Its unprecedented quality enables it to visualize cellular organelles and macromolecular complexes at nanometer resolution with native conformations. Motivated by developments in nanotechnology and machine learning, establishing machine learning approaches such as classification, detection and averaging for Cryo-ET image analysis has inspired broad interest. Yet, deep learning-based methods for biomedical imaging typically require large labeled datasets for good results, which can be a great challenge due to the expense of obtaining and labeling training data. To deal with this problem, we propose a generative model to simulate Cryo-ET images efficiently and reliably: CryoETGAN. This cycle-consistent and Wasserstein generative adversarial network (GAN) is able to generate images with an appearance similar to the original experimental data. Quantitative and visual grading results on generated images are provided to show that the results of our proposed method achieve better performance compared to the previous state-of-the-art simulation methods. Moreover, CryoETGAN is stable to train and capable of generating plausibly diverse image samples.

DEEP ACTIVE LEARNING FOR CRYO-ELECTRON TOMOGRAPHY CLASSIFICATION.

Cryo-Electron Tomography (cryo-ET) is an emerging 3D imaging technique which shows great potentials in structural biology research. One of the main challenges is to perform classification of macromolecules captured by cryo-ET. Recent efforts exploit deep learning to address this challenge. However, training reliable deep models usually requires a huge amount of labeled data in supervised fashion. Annotating cryo-ET data is arguably very expensive. Deep Active Learning (DAL) can be used to reduce labeling cost while not sacrificing the task performance too much. Nevertheless, most existing methods resort to auxiliary models or complex fashions (e.g. adversarial learning) for uncertainty estimation, the core of DAL. These models need to be highly customized for cryo-ET tasks which require 3D networks, and extra efforts are also indispensable for tuning these models, rendering a difficulty of deployment on cryo-ET tasks. To address these challenges, we propose a novel metric for data selection in DAL, which can also be leveraged as a regularizer of the empirical loss, further boosting the task model. We demonstrate the superiority of our method via extensive experiments on both simulated and real cryo-ET datasets. Our source Code and Appendix can be found at this URL.

Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization.

MOTIVATION: Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms. RESULTS: In this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with 'warp' modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data. AVAILABILITYAND IMPLEMENTATION: https://github.com/xulabs/aitom. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography.

Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecule structures, diverse macromolecules captured by the cellular tomograms need to be accurately classified. However, due to the poor signal-to-noise-ratio (SNR) and severe ray artifacts in the tomogram, it remains a major challenge to classify macromolecules with high accuracy. In this paper, we propose a new convolutional neural network, named 3D-Dilated-DenseNet, to improve the performance of macromolecule classification. In 3D-Dilated-DenseNet, there are two key strategies to guarantee macromolecule classification accuracy: 1) Using dense connections to enhance feature map utilization (corresponding to the baseline 3D-C-DenseNet); 2) Adopting dilated convolution to enrich multi-level information in feature maps. We tested 3D-Dilated-DenseNet and 3D-C-DenseNet both on synthetic data and experimental data. The results show that, on synthetic data, compared with the state-of-the-art method in the SHREC contest (SHREC-CNN), both 3D-C-DenseNet and 3D-Dilated-DenseNet outperform SHREC-CNN. In particular, 3D-Dilated-DenseNet improves 0.393 of F1 metric on tiny-size macromolecules and 0.213 on small-size macromolecules. On experimental data, compared with 3D-C-DenseNet, 3D-Dilated-DenseNet can increase classification performance by 2.1 percent.

Simultaneous estimation of cluster number and feature sparsity in high-dimensional cluster analysis.

Estimating the number of clusters (K) is a critical and often difficult task in cluster analysis. Many methods have been proposed to estimate K, including some top performers using resampling approach. When performing cluster analysis in high-dimensional data, simultaneous clustering and feature selection is needed for improved interpretation and performance. To our knowledge, little has been studied for simultaneous estimation of K and feature sparsity parameter in a high-dimensional exploratory cluster analysis. In this paper, we propose a resampling method to bridge this gap and evaluate its performance under the sparse K-means clustering framework. The proposed target function balances between sensitivity and specificity of clustering evaluation of pairwise subjects from clustering of full and subsampled data. Through extensive simulations, the method performs among the best over classical methods in estimating K in low-dimensional data. For high-dimensional simulation data, it also shows superior performance to simultaneously estimate K and feature sparsity parameter. Finally, we evaluated the methods in four microarray, two RNA-seq, one SNP, and two nonomics datasets. The proposed method achieves better clustering accuracy with fewer selected predictive genes in almost all real applications.

Pharmacokinetics/pharmacometabolomics-pharmacodynamics reveals the synergistic mechanism of a multicomponent herbal formula, Baoyuan decoction against cardiac hypertrophy.

Multicomponent herbal formulas (MCHFs) have earned a wide reputation for their definite efficacy in preventing or treating chronic complex diseases. However, holistic elucidation of the causal relationship between the bioavailable ingredients of MCHFs and their multitarget interactions is very challenging. To solve this problem, pharmacokinetics/pharmacometabolomics-pharmacodynamics (PK/PM-PD) combined with a multivariate biological correlation-network strategy was developed and applied to a classic MCHF, Baoyuan decoction (BYD), to clarify its active components and synergistic mechanism against cardiac hypertrophy (CH). First, multiple plasma metabolic biomarkers for ß-adrenergic agonist-induced CH rats were identified by using untargeted metabolomic profiling, and then, these CH-associated endogenous metabolites and the absorbed BYD-compounds in plasma at different treatment stages after oral administration of BYD were analyzed by using targeted PK and PM. Second, the dynamic relationship of BYD-related compounds and CH-associated endogenous metabolites and signaling pathways was built by using multivariate and bioinformatic correlation analysis. Finally, metabolic-related PD indicators were predicted and further verified by biological tests. The results demonstrated that the bioavailable BYD-compounds, such as saponins and flavonoids, presented differentiated and distinctive metabolic features and showed positive or negative correlations with various CH-altered metabolites and PD-indicators related to gut microbiota metabolism, amino acid metabolism, lipid metabolism, energy homeostasis, and oxidative stress at different treatment stages. This study provides a novel strategy for investigating the dynamic interaction between BYD and the biosystem, providing unique insight for disclosing the active components and synergistic mechanisms of BYD against CH, which also supplies a reference for other MCHF related research.