Interactions between Paramyxoviruses and Bacteria: Implications for Pathogenesis and Intervention

Globally, respiratory tract diseases caused by bacteria and viruses are an important burden of disease. Respiratory bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis and Staphyl

Mobile Services Meet Distributed Cloud: Benefits, Applications, and Challenges

As the explosive growth of smart devices and enormous new applications, the variety of corresponding cloud services has been growing quickly. The conventional centralized cloud was faced with an overhead on backhaul links and high latency. Accordingly, a decentralized cloud paradigm including edge computing, mobile edge computing, cloudlet, and so on, was introduced to distribute cloud services to the edge network which located in proximity to mobile devices few years ago. However, this paradigm was not paid attention at that time since cloud technology and mobile network communication were immature to motivate mobile services. Recently, with the overwhelming growth of mobile communication technology and cloud technology, distributed cloud is emerging as a paradigm well equipped with technologies to support a broad range of mobile services. The 5G mobile communication technology provides high-speed data and low latency. Cloud services can be automatically deployed in the edge networks quickly and easily. Distributed cloud can prove itself to bring many benefits for mobile service such as reducing network latency, as well as computational and network overhead at the central cloud. Besides, we present some applications to emphasize the necessity of distributed cloud for mobile service and discuss further technical challenges in distributed cloud

Program Management Integrated with Data and Decision Sciences

Program management (PM) complexity depends on the budget size and program types. In general, the program types can be classified into three categories, namely, defense, commercial, and civilian types. This chapter presents and discusses an approach for integrating the PM discipline areas with emerging data science and decision science1 (DDS) for any program type. Additionally, we describe the key PM areas and present a corresponding generalized model consists of a list of multiple PM discipline areas that can be tailored for any program types. To demonstrate the PM-DDS integration approach, we focus on three key PM areas and corresponding PM discipline areas related to schedule, cost, and risk management. These three discipline areas are analyzed to identify appropriate program elements that can be enhanced using existing DDS technology enablers (TEs). We also propose a flexible PM-DSS integration framework by leveraging existing machine learning operations (MLOps) framework. The proposed integration framework is expected to allow for enhancing the program planning and execution by reducing the program risk using a wide range of DDS TEs, including big data analytics, artificial intelligence, machine learning, deep learning, neural networks, and artificial intelligent

An outbreak of severe respiratory tract infection caused by human metapneumovirus in a residential care facility for elderly in Utrecht, the Netherlands, January to March 2010

Recognition of infections with human metapneumovirus (HMPV) among institutionalised elderly is rising. When HMPV was found to be the causative agent of an outbreak of pneumonia in a residential care facility for elderly in the Netherlands, an elaborate outbreak investigation was set up, including active surveillance for new cases. From clinical cases, defined by fever (> 38°C) and symptoms of respiratory tract infections, respiratory samples for analyses of viral pathogens by real-time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR) and blood samples for determination of HMPV-specific IgM and IgG antibody titres were taken. Five staff members and 18 residents fulfilled the clinical case definition. Of those, five residents tested positive for HMPV by rRT-PCR. The combination of rRT-PCR and serology identified nine confirmed cases, six probable cases, six possible cases and ruled out two persons as cases. Among residents, the outbreak of HMPV had an attack rate, ranging from 5% for laboratory-confirmed cases, to 13% for clinical cases. This outbreak investigation shows that HMPV is a potential serious pathogen for institutionalised elderly

Streptococcus pneumoniae enhances human respiratory syncytial virus infection in vitro and in vivo

Human respiratory syncytial virus (HRSV) and Streptococcus pneumoniae are important causative agents of respiratory tract infections. Both pathogens are associated with seasonal disease outbreaks in the pediatric population, and can often be detected simultaneously in infants hospitalized with bronchiolitis or pneumonia. It has been described that respiratory virus infections may predispose for bacterial superinfections, resulting in severe disease. However, studies on the influence of bacterial colonization of the upper respiratory tract on the pathogenesis of subsequent respiratory virus infections are scarce. Here, we have investigated whether pneumococcal colonization enhances subsequent HRSV infection. We used a newly generated recombinant subgroup B HRSV strain that expresses enhanced green fluorescent protein and pneumococcal isolates obtained from healthy children in disease-relevant in vitro and in vivo model systems. Three pneumococcal strains specifically enhanced in vitro HRSV infection of primary well-differentiated normal human bronchial epithelial cells grown at air-liquid interface, whereas two other strains did not. Since previous studies reported that bacterial neuraminidase enhanced HRSV infection in vitro, we measured pneumococcal neuraminidase activity in these cultures but found no correlation with the observed infection enhancement in our model. Subsequently, a selection of pneumococcal strains was used to induce nasal colonization of cotton rats, the best available small animal model for HRSV. Intranasal HRSV infection three days later resulted in strain-specific enhancement of HRSV replication in vivo. One S. pneumoniae strain enhanced HRSV both in vitro and in vivo, and was also associated with enhanced syncytium formation in vivo. However, neither pneumococci nor HRSV were found to spread from the upper to the lower respiratory tract, and neither pathogen was transmitted to naive cage mates by direct contact. These results demonstrate that pneumococcal colonization can enhance subsequent HRSV infection, and provide tools for additional mechanistic and intervention studies

Ground-Based HPA Pre-Distorter Using Machine Learning and Artificial Intelligent for Satellite Communication Applications

This chapter describes an innovative design and implementation approach of a ground-based pre-distorter framework using machine learning and artificial intelligence (ML-AI) technology for high power amplifier (HPA) pre-distortion. The ML-AI technology enabler proposed is a combined multi-objective reinforce learning-and-adaptive neural network (MORL-ANN) and an operating environment predictor (OEP). The proposed framework addresses the signal distortions caused by a nonlinear HPA on the ground transmitter and a nonlinear HPA located at a satellite communication (SATCOM) transponder (TXDER). The TXDER’s HPA is assumed to operate under unknown conditions. The objective is twofold, namely, to demonstrate (i) an advanced decision science technique using ML-AI for future SATCOM applications and (ii) the feasibility of the proposed ground-based ML-AI framework using an end-to-end SATCOM emulator. A new OEP concept using a deterministic and Bayesian approach to improve the MORL-ANN pre-distorter (PD) performance will also be presented

Needle-free delivery of measles virus vaccine to the lower respiratory tract of non-human primates elicits optimal immunity and protection

Needle-free measles virus vaccination by aerosol inhalation has many potential benefits. The current standard route of vaccination is subcutaneous injection, whereas measles virus is an airborne pathogen. However, the target cells that support replication of live-attenuated measles virus vaccines in the respiratory tract are largely unknown. The aims of this study were to assess the in vivo tropism of live-attenuated measles virus and determine whether respiratory measles virus vaccination should target the upper or lower respiratory tract. Four groups of twelve cynomolgus macaques were immunized with 104 TCID50 of recombinant measles virus vaccine strain Edmonston-Zagreb expressing enhanced green fluorescent protein. The vaccine virus was grown in MRC-5 cells and formulated with identical stabilizers and excipients as used in the commercial MVEZ vaccine produced by the Serum Institute of India. Animals were immunized by hypodermic injection, intra-tracheal inoculation, intra-nasal instillation, or aerosol inhalation. In each group six animals were euthanized at early time points post-vaccination, whereas the other six were followed for 14 months to assess immunogenicity and protection from challenge infection with wild-type measles virus. At early time-points, enhanced green fluorescent protein-positive measles virus-infected cells were detected locally in the muscle, nasal tissues, lungs, and draining lymph nodes. Systemic vaccine virus replication and viremia were virtually absent. Infected macrophages, dendritic cells and tissue-resident lymphocytes predominated. Exclusive delivery of vaccine virus to the lower respiratory tract resulted in highest immunogenicity and protection. This study sheds light on the tropism of a live-attenuated measles virus vaccine and identifies the alveolar spaces as the optimal site for respiratory delivery of measles virus vaccine

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived \u201ccomputational stain\u201d developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles