Prediction and interpretation of cancer survival using graph convolution neural networks.

The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.

Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles.

BACKGROUND: Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. RESULTS: We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. CONCLUSION: Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

Sacred Spaces: Religious and Secular Coping and Family Relationships in the Neonatal Intensive Care Unit.

BACKGROUND: Preterm birth is an unanticipated and stressful event for parents. In addition, the unfamiliar setting of the intensive care nursery necessitates strategies for coping. PURPOSE: The primary study objective of this descriptive study was to determine whether secular and religious coping strategies were related to family functioning in the neonatal intensive care unit. METHODS: Fifty-two parents of preterm (25-35 weeks' gestation) infants completed the Brief COPE (secular coping), the Brief RCOPE (religious coping), and the Family Environment Scale within 1 week of their infant's hospital admission. FINDINGS: This descriptive study found that parents' religious and secular coping was significant in relation to family relationship functioning. Specifically, negative religious coping (ie, feeling abandoned or angry at God) was related to poorer family cohesion and use of denial. IMPLICATIONS FOR PRACTICE: These findings have relevance for interventions focused toward enhancing effective coping for families. IMPLICATIONS FOR RESEARCH: Further study of religious and secular coping strategies for neonatal intensive care unit families is warranted in a larger more diverse sample of family members.

Classification of Cancer Types Using Graph Convolutional Neural Networks.

BACKGROUND: Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently. RESULTS: In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific. CONCLUSION: Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.

A 3D-printed broadband millimeter wave absorber.

We present the design, manufacturing technique, and characterization of a 3D-printed broadband graded index millimeter wave absorber. The absorber is additively manufactured using a fused filament fabrication 3D printer out of a carbon-loaded high impact polystyrene filament and is designed using a space-filling curve to optimize manufacturability using the said process. The absorber's reflectivity is measured from 63 GHz to 115 GHz and from 140 GHz to 215 GHz and is compared to electromagnetic simulations. The intended application is for terminating stray light in cosmic microwave background telescopes, and the absorber has been shown to survive cryogenic thermal cycling.

RNA sequencing reveals upregulation of a transcriptomic program associated with stemness in metastatic prostate cancer cells selected for taxane resistance.

Patients with metastatic castration-resistant prostate cancer (mCRPC) develop resistance to conventional therapies including docetaxel (DTX). Identifying molecular pathways underlying DTX resistance is critical for developing novel combinatorial therapies to prevent or reverse this resistance. To identify transcriptomic signatures associated with acquisition of chemoresistance we profiled gene expression in DTX-sensitive and -resistant mCRPC cells using RNA sequencing (RNA-seq). PC3 and DU145 cells were selected for DTX resistance and this phenotype was validated by immunoblotting using DTX resistance markers (e.g. clusterin, ABCB1/P-gp, and LEDGF/p75). Overlapping genes differentially regulated in the DTX-sensitive and -resistant cells were ranked by Gene Set Enrichment Analysis (GSEA) and validated to correlate transcript with protein expression. GSEA revealed that genes associated with cancer stem cells (CSC) (e.g., NES, TSPAN8, DPPP, DNAJC12, and MYC) were highly ranked and comprised 70% of the top 25 genes differentially upregulated in the DTX-resistant cells. Established markers of epithelial-to-mesenchymal transition (EMT) and CSCs were used to evaluate the stemness of adherent DTX-resistant cells (2D cultures) and tumorspheres (3D cultures). Increased formation and frequency of cells expressing CSC markers were detected in DTX-resistant cells. DU145-DR cells showed a 2-fold increase in tumorsphere formation and increased DTX resistance compared to DU145-DR 2D cultures. These results demonstrate the induction of a transcriptomic program associated with stemness in mCRPC cells selected for DTX resistance, and strengthen the emerging body of evidence implicating CSCs in this process. In addition, they provide additional candidate genes and molecular pathways for potential therapeutic targeting to overcome DTX resistance.

Using auditory-visual speech to probe the basis of noise-impaired consonant-vowel perception in dyslexia and auditory neuropathy.

Both dyslexics and auditory neuropathy (AN) subjects show inferior consonant-vowel (CV) perception in noise, relative to controls. To better understand these impairments, natural acoustic speech stimuli that were masked in speech-shaped noise at various intensities were presented to dyslexic, AN, and control subjects either in isolation or accompanied by visual articulatory cues. AN subjects were expected to benefit from the pairing of visual articulatory cues and auditory CV stimuli, provided that their speech perception impairment reflects a relatively peripheral auditory disorder. Assuming that dyslexia reflects a general impairment of speech processing rather than a disorder of audition, dyslexics were not expected to similarly benefit from an introduction of visual articulatory cues. The results revealed an increased effect of noise masking on the perception of isolated acoustic stimuli by both dyslexic and AN subjects. More importantly, dyslexics showed less effective use of visual articulatory cues in identifying masked speech stimuli and lower visual baseline performance relative to AN subjects and controls. Last, a significant positive correlation was found between reading ability and the ameliorating effect of visual articulatory cues on speech perception in noise. These results suggest that some reading impairments may stem from a central deficit of speech processing.