Enhancing Pandemic Prediction: A Deep Learning Approach Using Transformer Neural Networks and Multi-Source Data Fusion for Infectious Disease Forecasting.

The Covid-19 pandemic has highlighted the urgent need for accurate prediction of pandemic trends. We propose a deep learning model for predicting Covid-19 cases and deaths at the county level through transformer neural networks with multi-source data fusion, incorporating historical case data, death data, and social media sentiment analysis to capture both temporal (historical trends) and spatial (geographical relationships) dynamics within time series data. Additionally, we develop multi-level and multi-scale attention mechanisms for adaptive time-frequency analysis.

Distributed wireless power transfer based on secondary-side-direct-series topology for integration with building-attached photovoltaic system.

Building-attached photovoltaic (BAPV) systems integrate photovoltaic (PV) technology with buildings, attracting increasing attention as a promising solution to advancing eco-friendly objectives. In BAPV systems, wireless power transfer (WPT) technology offers unique advantages; however, existing research faces challenges in effectively balancing cost, efficiency, and flexibility. This paper presents a distributed WPT system based on secondary-side-direct-series (SSDS) topology.

Transformer-based conditional generative transfer learning network for cross domain fault diagnosis under limited data.

Most current generative adversarial network (GAN) cannot simultaneously consider the quality and diversity of generated samples due to limited data and variable working condition. To solve the problem, a Transformer-based conditional GAN transfer learning network is proposed. Firstly, a transformer-based conditional GAN (TCGAN) generative network is constructed with sample label information, enhancing the quality of generated data while retaining the diversity of generated signals.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer.

Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC).

Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC.

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.

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.

Mapping the Germline and Somatic Mutation Interaction Landscape in Indolent and Aggressive Prostate Cancers.

Background: A majority of prostate cancers (PCas) are indolent and cause no harm even without treatment. However, a significant proportion of patients with PCa have aggressive tumors that progress rapidly to metastatic disease and are often lethal. PCa develops through somatic mutagenesis, but emerging evidence suggests that germline genetic variation can markedly contribute to tumorigenesis.

Deconvolution of the Genomic and Epigenomic Interaction Landscape of Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. Emerging evidenced suggests that both genetics and epigenetic factors play a role in the pathogenesis of TNBC. However, oncogenic interactions and cooperation between genomic and epigenomic variation have not been characterized.

An improved local mean decomposition method based on improved composite interpolation envelope and its application in bearing fault feature extraction.

In order to overcome the influence of non-adaptive selection of non-stationary coefficient threshold of compound interpolation envelope (CIE) method on decomposition performance of local mean decomposition (LMD), a LMD method based on improved compound interpolation envelope (ICIE) is proposed in this paper. Firstly, combining the CIE with fractal box dimension, an improved envelope processing method, named ICIE, is proposed. Secondly, an improved LMD-based ICIE is presented and abbreviated as ICIELMD.