Integrating germline and somatic variation information using genomic data for the discovery of biomarkers in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is the most common diagnosed malignancy and the second leading cause of cancer-related deaths among men in the United States. High-throughput genotyping has enabled discovery of germline genetic susceptibility variants (herein referred to as germline mutations) associated with an increased risk of developing PCa. However, germline mutation information has not been leveraged and integrated with information on acquired somatic mutations to link genetic susceptibility to tumorigenesis. The objective of this exploratory study was to address this knowledge gap. METHODS: Germline mutations and associated gene information were derived from genome-wide association studies (GWAS) reports. Somatic mutation and gene expression data were derived from 495 tumors and 52 normal control samples obtained from The Cancer Genome Atlas (TCGA). We integrated germline and somatic mutation information using gene expression data. We performed enrichment analysis to discover molecular networks and biological pathways enriched for germline and somatic mutations. RESULTS: We discovered a signature of 124 genes containing both germline and somatic mutations. Enrichment analysis revealed molecular networks and biological pathways enriched for germline and somatic mutations, including, the PDGF, P53, MYC, IGF-1, PTEN and Androgen receptor signaling pathways. CONCLUSION: Integrative genomic analysis links genetic susceptibility to tumorigenesis in PCa and establishes putative functional bridges between the germline and somatic variation, and the biological pathways they control.

Condition Classification of Water-Filled Underground Siphon Using Acoustic Sensors.

Siphons have been widely used in water supply systems and sewage networks. However, it is difficult to implement non-destructive testing due to structural complexity and limited accessibility. In this paper, a novel condition classification method for water-filled underground siphons is proposed, which uses the acoustic signals received from acoustic sensors installed in the siphon. The proposed method has the advantages of simpler operation, lower cost, and higher detection efficiency. The acoustic wave forms in the siphons reflect on the system characteristics. Seven typical conditions of a water-filled underground siphon were investigated, and a series of experiments were conducted. Acoustic signals were recorded and transformed into acoustic pressure responses for further analysis. The variational mode decomposition (VMD) and the acoustic energy flow density were used for signal processing and feature extraction. The acoustic energy flux density eigenvectors were input to three different classifiers to classify the siphon conditions. The results demonstrate that the proposed acoustic-based approach can effectively classify the blockage and damage conditions of siphons, and the recognition accuracy of the proposed method is higher than 94.4%. Therefore, this research has value for engineering applications.

An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform.

The Empirical Wavelet Transform (EWT), which has a reliable mathematical derivation process and can adaptively decompose signals, has been widely used in mechanical applications, EEG, seismic detection and other fields. However, the EWT still faces the problem of how to optimally divide the Fourier spectrum during the application process. When there is noise interference in the analyzed signal, the parameterless scale-space histogram method will divide the spectrum into a variety of narrow bands, which will weaken or even fail to extract the fault modulation information. To accurately determine the optimal resonant demodulation frequency band, this paper proposes a method for applying Adaptive Average Spectral Negentropy (AASN) to EWT analysis (AEWT): Firstly, the spectrum is segmented by the parameterless clustering scale-space histogram method to obtain the corresponding empirical mode. Then, by comprehensively considering the Average Spectral Negentropy (ASN) index and correlation coefficient index on each mode, the correlation coefficient is used to adjust the ASN value of each mode, and the IMF with the highest value is used as the center frequency band of the fault information. Finally, a new resonant frequency band is reconstructed for the envelope demodulation analysis. The experimental results of different background noise intensities show that the proposed method can effectively detect the repetitive transients in the signal.

MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings.

In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion Esum(t) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal x_new(t). Secondly, the MOMEDA method is introduced to analyze x_new(t), extract the fault period impulse component x_cov(t), which is submerged by noise, and demodulate the signal x_cov(t) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings.

A Gated Recurrent Generative Transfer Learning Network for Fault Diagnostics Considering Imbalanced Data and Variable Working Conditions.

Transfer learning (TL) and generative adversarial networks (GANs) have been widely applied to intelligent fault diagnosis under imbalanced data and different working conditions. However, the existing data synthesis methods focus on the overall distribution alignment between the generated data and real data, and ignore the fault-sensitive features in the time domain, which results in losing convincing temporal information for the generated signal. For this reason, a novel gated recurrent generative TL network (GRGTLN) is proposed. First, a smooth conditional matrix-based gated recurrent generator is proposed to extend the imbalanced dataset. It can adaptively increase the attention of fault-sensitive features in the generated sequence. Wasserstein distance (WD) is introduced to enhance the construction of mapping relationships to promote data generation ability and transfer performance of the fault diagnosis model. Then, an iterative "generation-transfer" co-training strategy is developed for continuous parallel training of the model and the parameter optimization. Finally, comprehensive case studies demonstrate that GRGTLN can generate high-quality data and achieve satisfactory cross-domain diagnosis accuracy.

Repurposing a plant alkaloid homoharringtonine targets insulinoma associated-1 in N-Myc-activated neuroblastoma.

High-risk neuroblastoma (NB) is a heterogeneous and malignant childhood cancer that is frequently characterized by MYCN proto-oncogene amplification or elevated N-Myc protein (N-Myc) expression. An N-Myc downstream target gene, insulinoma associated-1 (INSM1) has emerged as a biomarker that plays a critical role in facilitating NB tumor cell growth and transformation. N-Myc activates endogenous INSM1 gene expression through binding to the E2-box of the INSM1 proximal promoter in NB. We identified a plant alkaloid, homoharringtonine (HHT), from a chemical library screening showing potent inhibition of INSM1 promoter activity. This positive-hit plant alkaloid exemplifies an effective screening approach for repurposed compound targeting INSM1 expression in NB cancer therapy. The elevated N-Myc and INSM1 expression in NB constitutes a positive-loop through INSM1 activation that promotes N-Myc stability. In the present study, the biological effects and anti-tumor properties of HHT against NB were examined. HHT either down regulates and/or interferes with the binding of N-Myc to the E2-box of the INSM1 promoter and the inhibition of PI3K/AKT-mediated N-Myc stability could lead to the NB cell apoptosis. HHT inhibition of NB cell proliferation is consistent with the INSM1 expression as higher level of INSM1 exhibits a more sensitive IC50 value. The combination treatment of HHT and A674563 provides a better option of increasing potency and reducing cellular cytotoxicity than HHT or A674563 treatment alone. Taken together, the suppression of the INSM1-associated signaling pathway axis promotes the inhibition of NB tumor cell growth. This study developed a feasible approach for repurposing an effective anti-NB drug.

HER3 targeting augments the efficacy of panobinostat in claudin-low triple-negative breast cancer cells.

Patients with triple-negative breast cancer (TNBC) have a poor prognosis and high relapse rate due to limited therapeutic options. This study was conducted to determine the mechanisms of action of panobinostat, a pan-inhibitor of histone deacetylase (HDAC) and FDA-approved medication for multiple myeloma, in TNBC and to provide a rationale for effective drug combinations against this aggressive disease. RNA sequencing analyses of the claudin-low (CL) TNBC (MDA-MB-231) cells untreated or treated with panobinostat were performed to identify the differentially expressed genes. Adaptive alterations in gene expression were analyzed and validated in additional CL TNBC cells. Tumor xenograft models were used to test the in vivo antitumor activity of panobinostat alone or its combinations with gefitinib, an EGFR-tyrosine kinase inhibitor (TKI). Panobinostat potently inhibited proliferation and induced apoptosis in all TNBC cells tested. However, in CL TNBC cells, this HDAC inhibitor markedly enhanced expression of HER3, which interacted with EGFR to activate both receptors and Akt signaling pathways. Combinations of panobinostat and gefitinib synergistically suppressed CL TNBC cell proliferation and promoted apoptosis in vitro and in vivo. Upregulation of HER3 compromises the efficacy of panobinostat in CL TNBC. Inactivation of HER3 combined with panobinostat represents a practical approach to combat CL TNBC.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker.

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.

Landscape of Molecular Crosstalk Perturbation between Lung Cancer and COVID-19.

BACKGROUND: Lung cancer patients have the worst outcomes when affected by coronavirus disease 2019 (COVID-19). The molecular mechanisms underlying the association between lung cancer and COVID-19 remain unknown. The objective of this investigation was to determine whether there is crosstalk in molecular perturbation between COVID-19 and lung cancer, and to identify a molecular signature, molecular networks and signaling pathways shared by the two diseases. METHODS: We analyzed publicly available gene expression data from 52 severely affected COVID-19 human lung samples, 594 lung tumor samples and 54 normal disease-free lung samples. We performed network and pathways analysis to identify molecular networks and signaling pathways shared by the two diseases. RESULTS: The investigation revealed a signature of genes associated with both diseases and signatures of genes uniquely associated with each disease, confirming crosstalk in molecular perturbation between COVID-19 and lung cancer. In addition, the analysis revealed molecular networks and signaling pathways associated with both diseases. CONCLUSIONS: The investigation revealed crosstalk in molecular perturbation between COVID-19 and lung cancer, and molecular networks and signaling pathways associated with the two diseases. Further research on a population impacted by both diseases is recommended to elucidate molecular drivers of the association between the two diseases.

Integrating Genomic Information with Tumor-Immune Microenvironment in Triple-Negative Breast Cancer.

BACKGROUND: the development and progression of triple-negative breast cancer (TNBC) is driven by somatic driver mutations and the tumor-immune microenvironment. To date, data on somatic mutations has not been leveraged and integrated with information on the immune microenvironment to elucidate the possible oncogenic interactions and their potential effects on clinical outcomes. Here, we investigated possible oncogenic interactions between somatic mutations and the tumor-immune microenvironment, and their correlation with patient survival in TNBC. METHODS: We performed analysis combining data on 7,875 somatic mutated genes with information on 1,751 immune-modulated genes, using gene-expression data as the intermediate phenotype, and correlated the resulting information with survival. We conducted functional analysis to identify immune-modulated molecular networks and signaling pathways enriched for somatic mutations likely to drive clinical outcomes. RESULTS: We discovered differences in somatic mutation profiles between patients who died and those who survived, and a signature of somatic mutated immune-modulated genes transcriptionally associated with TNBC, predictive of survival. In addition, we discovered immune-modulated molecular networks and signaling pathways enriched for somatic mutations. CONCLUSIONS: The investigation revealed possible oncogenic interactions between somatic mutations and the tumor-immune microenvironment in TNBC, likely to affect clinical outcomes.

Unknown System Dynamics Estimator for Active Vehicle Suspension Control Systems With Time-Varying Delay.

This article proposes a novel control method for vehicle active suspension systems in the presence of time-varying input delay and unknown nonlinearities. An unknown system dynamics estimator (USDE), which employs first-order low-pass filter operations and has only one tuning parameter, is constructed to deal with unknown nonlinearities. With this USDE, the widely used function approximators (e.g., neural networks and fuzzy-logic systems) are not needed, and the intermediate variables and observer used in the traditional estimators are not required. This estimator has a reduced computational burden, trivial parameter tuning and guaranteed convergence. Moreover, a predictor-based compensation strategy is developed to handle the time-varying input delay. Finally, we combine the suggested USDE and predictor to design a feedback controller to attenuate the vibrations of vehicle body and retain the required suspension performances. Theoretical analysis is carried out via the Lyapunov-Krasovkii functional to prove the stability of the closed-loop system. Simulation results based on professional vehicle simulation software Carsim are provided to show the efficiency of the proposed control scheme.

CpG Site-Based Signature Predicts Survival of Colorectal Cancer.

BACKGROUND: A critical unmet medical need in clinical management of colorectal cancer (CRC) pivots around lack of noninvasive and or minimally invasive techniques for early diagnosis and prognostic prediction of clinical outcomes. Because DNA methylation can capture the regulatory landscape of tumors and can be measured in body fluids, it provides unparalleled opportunities for the discovery of early diagnostic and prognostics markers predictive of clinical outcomes. Here we investigated use of DNA methylation for the discovery of potential clinically actionable diagnostic and prognostic markers for predicting survival in CRC. METHODS: We analyzed DNA methylation patterns between tumor and control samples to discover signatures of CpG sites and genes associated with CRC and predictive of survival. We conducted functional analysis to identify molecular networks and signaling pathways driving clinical outcomes. RESULTS: We discovered a signature of aberrantly methylated genes associated with CRC and a signature of thirteen (13) CpG sites predictive of survival. We discovered molecular networks and signaling pathways enriched for CpG sites likely to drive clinical outcomes. CONCLUSIONS: The investigation revealed that CpG sites can predict survival in CRC and that DNA methylation can capture the regulatory state of tumors through aberrantly methylated molecular networks and signaling pathways.

Breast Cancer Type Classification Using Machine Learning.

BACKGROUND: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. METHODS: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. RESULTS: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. CONCLUSIONS: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

Elucidation of the Genomic-Epigenomic Interaction Landscape of Aggressive Prostate Cancer.

BACKGROUND: Majority of prostate cancer (PCa) deaths are attributed to localized high-grade aggressive tumours which progress rapidly to metastatic disease. A critical unmet need in clinical management of PCa is discovery and characterization of the molecular drivers of aggressive tumours. The development and progression of aggressive PCa involve genetic and epigenetic alterations occurring in the germline, somatic (tumour), and epigenomes. To date, interactions between genes containing germline, somatic, and epigenetic mutations in aggressive PCa have not been characterized. The objective of this investigation was to elucidate the genomic-epigenomic interaction landscape in aggressive PCa to identify potential drivers aggressive PCa and the pathways they control. We hypothesized that aggressive PCa originates from a complex interplay between genomic (both germline and somatic mutations) and epigenomic alterations. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect gene expression, molecular networks, and signaling pathways which in turn drive aggressive PCa. METHODS: We addressed these hypotheses by performing integrative data analysis combining information on germline mutations from genome-wide association studies with somatic and epigenetic mutations from The Cancer Genome Atlas using gene expression as the intermediate phenotype. RESULTS: The investigation revealed signatures of genes containing germline, somatic, and epigenetic mutations associated with aggressive PCa. Aberrant DNA methylation had effect on gene expression. In addition, the investigation revealed molecular networks and signalling pathways enriched for germline, somatic, and epigenetic mutations including the STAT3, PTEN, PCa, ATM, AR, and P53 signalling pathways implicated in aggressive PCa. CONCLUSIONS: The study demonstrated that integrative analysis combining diverse omics data is a powerful approach for the discovery of potential clinically actionable biomarkers, therapeutic targets, and elucidation of oncogenic interactions between genomic and epigenomic alterations in aggressive PCa.

Diagnosis-to-surgery interval and survival for different histologies of stage I-IIA lung cancer.

BACKGROUND: Guidelines on timeliness of lung cancer surgery are inconsistent. Lung cancer histologic subtypes have different prognosis and treatment. It is important to understand the consequences of delayed surgery for each lung cancer histologic subtype. This study aimed to examine the association between diagnosis-to-surgery time interval and survival for early stage lung cancer and selected histologic subtypes. METHODS: Patients diagnosed with stage I-IIA lung cancer between 2004 and 2015 receiving definitive surgery and being followed up until Dec. 31, 2018, were identified from Surveillance, Epidemiology, and End Results database. Histologic subtypes included adenocarcinoma, squamous or epidermoid carcinoma, bronchioloalveolar carcinoma, large cell carcinoma, adenosquamous carcinoma, carcinoid carcinoma, and small cell carcinoma. Diagnosis-to-surgery interval was treated as multi-categorical variables (<1, 1-2, 2-3, and ≥3 months) and binary variables (≥1 vs. <1 month, ≥2 vs. <2 months, and ≥3 vs. <3 months). Outcomes included cancer-specific and overall survival. Covariates included age at diagnosis, sex, race, marital status, tumor size, grade, surgery type, chemotherapy, radiotherapy, and study period. Kaplan-Meier survival curves and Cox proportional hazards regression models were applied to examine the survival differences. RESULTS: With a median follow-up time of 51 months, a total of 40,612 patients were analyzed, including 40.1% adenocarcinoma and 24.5% squamous or epidermoid carcinoma. The proportion of patients receiving surgery <1, 1-2, 2-3, and ≥3 months from diagnosis were 34.2%, 33.9%, 19.8%, and 12.1%, respectively. Delayed surgery was associated with worse cancer-specific and overall survival for all lung cancers, adenocarcinoma, squamous or epidermoid, bronchioloalveolar, and large cell carcinoma (20-40% increased risk). Dose-dependent effects (longer delay, worse survival) were observed in all lung cancers, adenocarcinoma, and squamous and epidermoid carcinoma. No significant association between surgery delay and survival was observed in adenosquamous, carcinoid, and small cell carcinoma. CONCLUSIONS: Our findings support the guidelines of undertaking surgery within 1 month from diagnosis in patients with stage I-IIA lung cancer. The observed dose-dependent effects emphasize the clinical importance of early surgery. Future studies with larger sample size of less frequent histologic subtypes are warranted to provide more evidence for histology-specific lung cancer treatment guidelines.

Racial differences in the risk of second primary bladder cancer following radiation therapy among localized prostate cancer patients.

OBJECTIVES: To investigate the race-specific second primary bladder cancer (SPBC) risk following prostatic irradiation. METHODS: Louisiana residents who were diagnosed with localized prostate cancer (PCa) in 1996-2013 and received surgery or radiation were included. Patients were followed until SPBC diagnosis, death, or Dec. 2018. The exposure variable was type of treatment (radiation only vs. surgery only). The outcome was time from PCa diagnosis to SPBC diagnosis, stratified by race. Fine and Gray's competing risk model was applied with death as a competing event and adjustment of sociodemographic and tumor characteristics. We used 5 years and 10 years as lag time in the analyses. RESULTS: A total of 26,277 PCa patients with a median follow-up of 10.7 years were analyzed, including 18,598 white and 7679 black patients. About 42.9 % of whites and 45.7 % of blacks received radiation. SPBC counted for 1.84 % in the radiation group and 0.90 % in the surgery group among white patients and for 0.91 % and 0.58 %, respectively, among black patients. The adjusted subdistribution hazard ratio of SPBC was 1.80 (95 % CI: 1.30-2.48) for radiation recipients compared to surgery recipients among white patients; 1.93 (95 % CI: 1.36-2.74) if restricted to external beam radiation therapy (EBRT). The SPBC risk was not significantly different between irradiated and surgically treated among blacks. CONCLUSIONS: The SPBC risk is almost two-fold among white irradiated PCa patients compared to their counterparts treated surgically. Our findings highlight the need for enhanced surveillance for white PCa survivors receiving radiotherapy, especially those received EBRT.

Racial Disparity and Social Determinants in Receiving Timely Surgery Among Stage I-IIIA Non-small Cell Lung Cancer Patients in a U.S. Southern State.

Delayed surgery is associated with worse lung cancer outcomes. Social determinants can influence health disparities. This study aimed to examine the potential racial disparity and the effects from social determinants on receipt of timely surgery among lung cancer patients in Louisiana, a southern state in the U.S. White and black stage I-IIIA non-small cell lung cancer patients diagnosed in Louisiana between 2004 and 2016, receiving surgical lobectomy or a more extensive surgery, were selected. Diagnosis-to-surgery interval >6 weeks were considered as delayed surgery. Social determinants included marital status, insurance, census tract level poverty, and census tract level urbanicity. Multivariable logistic regression and generalized multiple mediation analysis were conducted. A total of 3,616 white (78.9%) and black (21.1%) patients were identified. The median time interval from diagnosis to surgery was 27 days in whites and 42 days in blacks (P < 0.0001). About 28.7% of white and 48.4% of black patients received delayed surgery (P < 0.0001). Black patients had almost two-fold odds of receiving delayed surgery than white patients (adjusted odds ratio: 1.91; 95% confidence interval: 1.59-2.30). Social determinants explained about 26% of the racial disparity in receiving delayed surgery. Having social support, private insurance, and living in census tracts with lower poverty level were associated with improved access to timely surgery. The census tract level poverty level a stronger effect on delayed surgery in black patients than in white patients. Tailored interventions to improve the timely treatment in NSCLC patients, especially black patients, are needed in the future.

Severe COVID-19 Is Characterized by an Impaired Type I Interferon Response and Elevated Levels of Arginase Producing Granulocytic Myeloid Derived Suppressor Cells.

COVID-19 ranges from asymptomatic in 35% of cases to severe in 20% of patients. Differences in the type and degree of inflammation appear to determine the severity of the disease. Recent reports show an increase in circulating monocytic-myeloid-derived suppressor cells (M-MDSC) in severe COVID 19 that deplete arginine but are not associated with respiratory complications. Our data shows that differences in the type, function and transcriptome of granulocytic-MDSC (G-MDSC) may in part explain the severity COVID-19, in particular the association with pulmonary complications. Large infiltrates by Arginase 1+ G-MDSC (Arg+G-MDSC), expressing NOX-1 and NOX-2 (important for production of reactive oxygen species) were found in the lungs of patients who died from COVID-19 complications. Increased circulating Arg+G-MDSC depleted arginine, which impaired T cell receptor and endothelial cell function. Transcriptomic signatures of G-MDSC from patients with different stages of COVID-19, revealed that asymptomatic patients had increased expression of pathways and genes associated with type I interferon (IFN), while patients with severe COVID-19 had increased expression of genes associated with arginase production, and granulocyte degranulation and function. These results suggest that asymptomatic patients develop a protective type I IFN response, while patients with severe COVID-19 have an increased inflammatory response that depletes arginine, impairs T cell and endothelial cell function, and causes extensive pulmonary damage. Therefore, inhibition of arginase-1 and/or replenishment of arginine may be important in preventing/treating severe COVID-19.

Nature of power electronics and integration of power conversion with communication for talkative power.

Power electronics and communication electronics are both based on electromagnetic theory, but they are usually regarded as two distinct subfields in electrical engineering. In fact, however, electric power is the most common matter-based carrier of messages; thus, power electronics and communications can be jointly considered. Here we study the essential nature of dc-dc power converters and characterize the similarity of their operation principle to that of communication systems. Based on this similarity and the double modulation methods used in power electronics and communication, a double modulation strategy for both power and data is presented and applied in dc-dc power converters to achieve what we call 'talkative power'. A modulation strategy called frequency hopping-differential phase shift keying (FH-DPSK) is also presented to overcome the crosstalk between chosen transmission systems. The proposed talkative power strategy sheds new light on and provides inspiration for the further development of power electronics and communication.

Delineation of the Germline and Somatic Mutation Interaction Landscape in Triple-Negative and Non-Triple-Negative Breast Cancer.

BACKGROUND: Breast cancer development and progression involve both germline and somatic mutations. High-throughput genotyping and next-generation sequencing technologies have enabled discovery of genetic risk variants and acquired somatic mutations driving the disease. However, the possible oncogenic interactions between germline genetic risk variants and somatic mutations in triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) have not been characterized. Here, we delineated the possible oncogenic interactions between genes containing germline and somatic mutations in TNBC and non-TNBC and investigated whether there are differences in gene expression and mutation burden between the two types of breast cancer. METHODS: We addressed this problem by integrating germline mutation information from genome-wide association studies with somatic mutation information from next-generation sequencing using gene expression data as the intermediated phenotype. We performed network and pathway analyses to discover molecular networks and signalling pathways enriched for germline and somatic mutations. RESULTS: The investigation revealed signatures of differentially expressed and differentially somatic mutated genes between TNBC and non-TNBC. Network and pathway analyses revealed functionally related genes interacting in gene regulatory networks and multiple signalling pathways enriched for germline and somatic mutations for each type of breast cancer. Among the signalling pathways discovered included the DNA repair and Androgen and ATM signalling pathways for TNBC and the DNA damage response, molecular mechanisms of cancer, and ATM and GP6 signalling pathways for non-TNBC. CONCLUSIONS: The results show that integrative genomics is a powerful approach for delineating oncogenic interactions between genes containing germline and genes containing somatic mutations in TNBC and non-TNBC and establishes putative functional bridges between genetic and somatic alterations and the pathways they control in the two types of breast cancer.