Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals. Convincingly, RETAIN achieved an AUC of 82% in comparison to an AUC of 79% for logistic regression, demonstrating the power of more expressive deep learning models for EHR predictive modeling. The prediction performance fluctuated across different patient groups and varied from hospital to hospital. Also, we trained RETAIN models on individual hospitals and found that the model can be applied to other hospitals with only about 3.6% of reduction of AUC. Our results demonstrated the capability of RNN for predictive modeling with large and heterogeneous EHR data, and pave the road for future improvements.

Photodynamic therapy (PDT), which utilizes light excited photosensitizers (PSs) to generate reactive oxygen species (ROS) and consequently ablate cancer cells or diseased tissue, has attracted a great deal of attention in the last decades due to its unique advantages. In order to further enhance PDT effect, PSs are functionalized to target specific sub‐cellular organelles, but most PSs cannot target nucleolus, which is demonstrated as a more efficient and ideal site for cancer treatment. Here, an effective carbon dots (C‐dots) photosensitizer with intrinsic nucleolus‐targeting capability, for the first time, is synthesized, characterized, and employed for in vitro and in vivo image‐guided photodynamic anticancer therapy with enhanced treatment performance at a low dose of PS and light irradiation. The C‐dots possess high ROS generation efficiency and fluorescence quantum yield, excellent in vitro and in vivo biocompatibility, and rapid renal clearance, endowing it with a great potential for future translational research.

Repair of large bone defects remains a challenge for surgeons, tissue engineering represents a promising approach. However, the use of this technique is limited by delayed vascularization in central regions of the scaffold. Growth differentiation factor 15(GDF15) has recently been reported to be a potential angiogenic cytokine and has an ability to promote the proliferation of human umbilical vein endothelial cells(HUVECs). Whether it can be applied for promoting vascularized bone regeneration is still unknown. In this study, we demonstrated that GDF15 augmented the expression of cyclins D1 and E, induced Rb phosphorylation and E2F-1 nuclear translocation, as well as increased HUVECs proliferation. Furthermore, we also observed that GDF15 promoted the formation of functional vessels at an artificially-induced angiogenic site, and remarkably improved the healing in the repair of critical-sized calvarial defects. Our results confirm the essential role of GDF15 in angiogenesis and suggest its potential beneficial use in regenerative medicine.

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments.

Heterochrony, the phylogenic change in the time of developmental events or rate of development, has been thought to play an important role in producing phenotypic novelty during evolution. Increasing evidence suggests that specific genes are implicated in heterochrony, guiding the process of developmental divergence, but no quantitative models have been instrumented to map such heterochrony genes. Here, we present a computational framework for genetic mapping by which to characterize and locate quantitative trait loci (QTLs) that govern heterochrony described by four parameters, the timing of the inflection point, the timing of maximum acceleration of growth, the timing of maximum deceleration of growth, and the length of linear growth. The framework was developed from functional mapping, a dynamic model derived to map QTLs for the overall process and pattern of development. By integrating an optimality algorithm, the framework allows the so-called heterochrony QTLs (hQTLs) to be tested and quantified. Specific pipelines are given for testing how hQTLs control the onset and offset of developmental events, the rate of development, and duration of a particular developmental stage. Computer simulation was performed to examine the statistical properties of the model and demonstrate its utility to characterize the effect of hQTLs on population diversification due to heterochrony. By analyzing a genetic mapping data in rice, the framework identified an hQTL that controls the timing of maximum growth rate and duration of linear growth stage in plant height growth. The framework provides a tool to study how genetic variation translates into phenotypic innovation, leading a lineage to evolve, through heterochrony.

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With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data. A two-stage hierarchical expectation–maximization (EM) algorithm is implemented to estimate an optimal number of groups and mean expression amounts of each group across two environments. A procedure is formulated to test whether and how a given group shows a plastic response to environmental changes. The impact of gene–environment interactions on the phenotypic plasticity of the organism can also be visualized and characterized. The model was used to analyse an RNA-seq dataset measured from two cell lines of breast cancer that respond differently to an anti-cancer drug, from which genes associated with the resistance and sensitivity of the cell lines are identified. We performed simulation studies to validate the statistical behaviour of the model. The model provides a useful tool for clustering gene expression data by RNA-seq, facilitating our understanding of gene functions and networks.