Chaotic dynamics for homeostatic hematopoiesis.

Hematopoiesis is a highly dynamical and stochastic process, challenging our understanding of homeostasis. Clinical studies of leukemia or neutropenic patients revealed that multiple blood cell types fluctuate spontaneously with large yet regular oscillations of their frequencies. Yet the stability of hematopoiesis in healthy individuals remains understudied.

Significant biomagnification of methylmercury in songbird nestlings through a rice-based food web: Insights from stable mercury isotopes.

To elucidate the sources and transfer of mercury (Hg) in terrestrial food chains, particularly in heavily Hg-contaminated rice paddy ecosystems, we collected rice leaves, invertebrates, and Russet Sparrow nestlings from a clear food chain and analyzed the dietary compositions and potential Hg sources using stable Hg isotopes coupled with a Bayesian isotope mixing model (BIMM). Our findings indicated that MeHg exposure is dominant through the dietary route, with caterpillars, grasshoppers, and katydids being the main prey items, while the less provisioned spiders, dragonflies, and mantises contributed the most of the Hg to nestlings. We found minimal MIF but certain MDF in this terrestrial food chain and identified two distinct MeHg sources of dietary exposure and maternal transfer.

TCR ligand potency differentially impacts PD-1 inhibitory effects on diverse signaling pathways.

Checkpoint blockade revolutionized cancer therapy, but we still lack a quantitative, mechanistic understanding of how inhibitory receptors affect diverse signaling pathways. To address this issue, we developed and applied a fluorescent intracellular live multiplex signal transduction activity reporter (FILMSTAR) system to analyze PD-1-induced suppressive effects. These studies identified pathways triggered solely by TCR or requiring both TCR and CD28 inputs.

Theoretical and computational tools to model multistable gene regulatory networks.

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges.

Theoretical and computational tools to model multistable gene regulatory networks.

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges.

Decoding the coupled decision-making of the epithelial-mesenchymal transition and metabolic reprogramming in cancer.

Cancer metastasis relies on an orchestration of traits driven by different interacting functional modules, including metabolism and epithelial-mesenchymal transition (EMT). During metastasis, cancer cells can acquire a hybrid metabolic phenotype (W/O) by increasing oxidative phosphorylation without compromising glycolysis and they can acquire a hybrid epithelial/mesenchymal (E/M) phenotype by engaging EMT. Both the W/O and E/M states are associated with high metastatic potentials, and many regulatory links coupling metabolism and EMT have been identified.

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019.

Breast cancer dormancy: need for clinically relevant models to address current gaps in knowledge.

Breast cancer is the most commonly diagnosed cancer in the USA. Although advances in treatment over the past several decades have significantly improved the outlook for this disease, most women who are diagnosed with estrogen receptor positive disease remain at risk of metastatic relapse for the remainder of their life. The cellular source of late relapse in these patients is thought to be disseminated tumor cells that reactivate after a long period of dormancy.

Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer.

Cancer cells have the plasticity to adjust their metabolic phenotypes for survival and metastasis. A developmental programme known as epithelial-to-mesenchymal transition (EMT) plays a critical role during metastasis, promoting the loss of polarity and cell-cell adhesion and the acquisition of motile, stem-cell characteristics. Cells undergoing EMT or the reverse mesenchymal-to-epithelial transition (MET) are often associated with metabolic changes, as the change in phenotype often correlates with a different balance of proliferation versus energy-intensive migration.

A Weighted Graph Attention Network Based Method for Multi-label Classification of Electrocardiogram Abnormalities.

The multi-label electrocardiogram (ECG) classification is to automatically predict a set of concurrent cardiac abnormalities in an ECG record, which is significant for clinical diagnosis. Modeling the cardiac abnormality dependencies is the key to improving classification performance. To capture the dependencies, we proposed a multi-label classification method based on the weighted graph attention networks.

Deep Multi-instance Networks for Bundle Branch Block Detection from Multi-lead ECG.

Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training.

Drug-Tolerant Idling Melanoma Cells Exhibit Theory-Predicted Metabolic Low-Low Phenotype.

Cancer cells adjust their metabolic profiles to evade treatment. Metabolic adaptation is complex and hence better understood by an integrated theoretical-experimental approach. Using a minimal kinetic model, we predicted a previously undescribed Low/Low (L/L) phenotype, characterized by low oxidative phosphorylation (OXPHOS) and low glycolysis.

Decoding the mechanisms underlying cell-fate decision-making during stem cell differentiation by random circuit perturbation.

Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method, ndom rcuit rturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified robust gene states.

A Novel Method for Calibration-Based Cuff-Less Blood Pressure Estimation.

Cuff-less blood pressure estimation technology is useful for cardiovascular disease monitoring. However, without calibration, cuff-less blood pressure estimation is hard to achieve clinical acceptable performance. The traditional methods are always calibrated with retraining.

Novel Deep Convolutional Neural Network for Cuff-less Blood Pressure Measurement Using ECG and PPG Signals.

Cuff-less blood pressure (BP) is a potential method for BP monitoring because it is undisturbed and continuous monitoring. Existing cuff-less estimation methods are easily influenced by signal noise and non-ideal signal morphology. In this study we propose a novel well-designed Convolutional Neural Network (CNN) model named Deep-BP for BP estimation task.

An Electrocardiogram Delineator via Deep Segmentation Network.

Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac diseases. We treat the ECG delineation task as an one-dimensional segmentation problem, and propose a novel end-to-end deep learning method to segment sections of ECG signal. Our neural network consists of two parts: a segmentation network composed of multiple 1D Convolutional Neural Networks (CNN) and a postprocessing network composed of a sequential Conditional Random Field (CRF).

Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal.

The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat.

A Novel Detection Method of Bundle Branch Block from Multi-lead ECG.

Bundle branch block (BBB) is a common conduction block disease and can be diagnosed using electrocardiogram (ECG) signal in clinical practice. In this paper, a novel method was proposed to detect two types of BBB: right BBB (RBBB) and left BBB (LBBB) based on the combination of deep features and several kinds of expert features. We evaluated the proposed method on the MIT-BIH Arrhythmia database (AR) and China Physiological Signal Challenge 2018 database (CPSC).

Quantifying Cancer Epithelial-Mesenchymal Plasticity and its Association with Stemness and Immune Response.

Cancer cells can acquire a spectrum of stable hybrid epithelial/mesenchymal (E/M) states during epithelial-mesenchymal transition (EMT). Cells in these hybrid E/M phenotypes often combine epithelial and mesenchymal features and tend to migrate collectively commonly as small clusters. Such collectively migrating cancer cells play a pivotal role in seeding metastases and their presence in cancer patients indicates an adverse prognostic factor.

Interconnected feedback loops among ESRP1, HAS2, and CD44 regulate epithelial-mesenchymal plasticity in cancer.

Aberrant activation of epithelial-mesenchymal transition (EMT) in carcinoma cells contributes to increased migration and invasion, metastasis, drug resistance, and tumor-initiating capacity. EMT is not always a binary process; rather, cells may exhibit a hybrid epithelial/mesenchymal (E/M) phenotype. ZEB1-a key transcription factor driving EMT-can both induce and maintain a mesenchymal phenotype.

Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways.

Metabolic plasticity enables cancer cells to switch their metabolism phenotypes between glycolysis and oxidative phosphorylation (OXPHOS) during tumorigenesis and metastasis. However, it is still largely unknown how cancer cells orchestrate gene regulation to balance their glycolysis and OXPHOS activities. Previously, by modeling the gene regulation of cancer metabolism we have reported that cancer cells can acquire a stable hybrid metabolic state in which both glycolysis and OXPHOS can be used.

Testing the gene expression classification of the EMT spectrum.

The epithelial-mesenchymal transition (EMT) plays a central role in cancer metastasis and drug resistance-two persistent clinical challenges. Epithelial cells can undergo a partial or full EMT, attaining either a hybrid epithelial/mesenchymal (E/M) or mesenchymal phenotype, respectively. Recent studies have emphasized that hybrid E/M cells may be more aggressive than their mesenchymal counterparts.

RACIPE: a computational tool for modeling gene regulatory circuits using randomization.

Background: One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks.

Results: We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters.