Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study

This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives

On a Service-Oriented Approach for an Engineering Knowledge Desktop

Increasingly, manufacturing companies are shifting their focus from selling products to providing services. As a result, when designing new products, engineers must increasingly consider the life cycle costs in addition to any design requirements. To identify possible areas of concern, designers are required to consult existing maintenance information from identical products. However, in a large engineering company, the amount of information available is significant and in wide range of formats. This paper presents a prototype knowledge desktop suitable for the design engineer. The Engineering Knowledge Desktop analyses and suggests relevant information from ontologically marked-up heterogeneous web resources. It is designed using a Service-Oriented Architecture, with an ontology to mediate between Web Services. It has been delivered to the user community for evaluation

Data Mining to Support Engineering Design Decision

The design and maintenance of an aero-engine generates a significant amount of documentation. When designing new engines, engineers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. Firstly, this paper investigate the use of advanced business intelligence tenchniques to solve the problem of knowledge transfer from maintenance to design of aeroengines. Based on data availability and quality, various models were deployed. An association model was used to uncover hidden trends among parts involved in maintenance events. Classification techniques comprising of various algorithms was employed to determine severity of events. Causes of high severity events that lead to major financial loss was traced with the help of summarization techniques. Secondly this paper compares and evaluates the business intelligence approach to solve the problem of knowledge transfer with solutions available from the Semantic Web. The results obtained provide a compelling need to have data mining support on RDF/OWL-based warehoused data

An operations and command systems for the extreme ultraviolet explorer

About 40% of the budget of a scientific spacecraft mission is usually consumed by Mission Operations & Data Analysis (MO&DA) with MO driving these costs. In the current practice, MO is separated from spacecraft design and comes in focus relatively late in the mission life cycle. As a result, spacecraft may be designed that are very difficult to operate. NASA centers have extensive MO expertise but often lessons learned in one mission are not exploited for other parallel or future missions. A significant reduction of MO costs is essential to ensure a continuing and growing access to space for the scientific community. We are addressing some of these issues with a highly automated payload operations and command system for an existing mission, the Extreme Ultraviolet Explorer (EUVE). EUVE is currently operated jointly by the Goddard Space Flight Center (GSFC), responsible for spacecraft operations, and the Center for Extreme Ultraviolet Astrophysics (CEA) of the University of California, Berkeley, which controls the telescopes and scientific instruments aboard the satellite. The new automated system is being developed by a team including personnel from the NASA Ames Research Center (ARC), the Jet Propulsion Laboratory (JPL) and the Center for EUV Astrophysics (CEA). An important goal of the project is to provide AI-based technology that can be easily operated by nonspecialists in AI. Another important goal is the reusability of the techniques for other missions. Models of the EUVE spacecraft need to be built both for planning/scheduling and for monitoring. In both cases, our modeling tools allow the assembly of a spacecraft model from separate sub-models of the various spacecraft subsystems. These sub-models are reusable; therefore, building mission operations systems for another small satellite mission will require choosing pre-existing modules, reparametrizing them with respect to the actual satellite telemetry information, and reassembling them in a new model. We briefly describe the EUVE mission and indicate why it is particularly suitable for the task. Then we briefly outline our current work in mission planning/scheduling and spacecraft and instrument health monitoring

Dose de-escalation of intrapleural tissue plasminogen activator therapy for pleural infection. The alteplase dose assessment for Pleural infection Therapy project

Rationale: Intrapleural therapy with a combination of tissue plasminogen activator (tPA) 10 mg and DNase 5 mg administered twice daily has been shown in randomized and open-label studies to successfully manage over 90% of patients with pleural infection without surgery. Potential bleeding risks associated with intrapleural tPA and its costs remain important concerns. The aim of the ongoing Alteplase Dose Assessment for Pleural infection Therapy (ADAPT) project is to investigate the efficacy and safety of dose de-escalation for intrapleural tPA. The first of several planned studies is presented here. Objectives: To evaluate the efficacy and safety of a reduced starting dose regimen of 5 mg of tPA with 5 mg of DNase administered intrapleurally for pleural infection. Methods: Consecutive patients with pleural infection at four participating centers in Australia, the United Kingdom, and New Zealand were included in this observational, open-label study. Treatment was initiated with tPA 5 mg and DNase 5 mg twice daily. Subsequent dose escalation was permitted at the discretion of the attending physician. Data relating to treatment success, radiological and systemic inflammatory changes (blood C-reactive protein), volume of fluid drained, length of hospital stay, and treatment complications were extracted retrospectively from the medical records. Results: We evaluated 61 patients (41 males; age, 57 ± 16 yr). Most patients (n = 58 [93.4%]) were successfully treated without requiring surgery for pleural infection. Treatment success was corroborated by clearance of pleural opacities visualized by chest radiography (from 42% [interquartile range, 22–58] to 16% [8–31] of hemithorax; P < 0.001), increase in pleural fluid drainage (from 175 ml in the 24 h preceding treatment to 2,025 ml [interquartile range, 1,247–2,984] over 72 h of therapy; P <  0.05) and a reduction in blood C-reactive protein (P < 0.05). Seven patients (11.5%) had dose escalation of tPA to 10 mg. Three patients underwent surgery. Three patients (4.9%) received blood transfusions for gradual pleural blood loss; none were hemodynamically compromised. Pain requiring escalation of analgesia affected 36% of patients; none required cessation of therapy. Conclusions: These pilot data suggest that a starting dose of 5 mg of tPA administered intrapleurally twice daily in combination with 5 mg of DNase for the treatment of pl

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies

BACKGROUND: Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA. RESULTS: Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R(2 )= 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index). CONCLUSION: In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies

Deficiency of Th17 cells in hyper IgE syndrome due to mutations in STAT3

Hyper–immunoglobulin E syndrome (HIES) is a primary immune deficiency characterized by abnormal and devastating susceptibility to a narrow spectrum of infections, most commonly Staphylococcus aureus and Candida albicans. Recent investigations have identified mutations in STAT3 in the majority of HIES patients studied. Despite the identification of the genetic cause of HIES, the mechanisms underlying the pathological features of this disease remain to be elucidated. Here, we demonstrate a failure of CD4+ T cells harboring heterozygous STAT3 mutations to generate interleukin 17–secreting (i.e., T helper [Th]17) cells in vivo and in vitro due to a failure to express sufficient levels of the Th17-specific transcriptional regulator retinoid-related orphan receptor γt. Because Th17 cells are enriched for cells with specificities against fungal antigens, our results may explain the pattern of infection susceptibility characteristic of patients with HIES. Furthermore, they underscore the importance of Th17 responses in normal host defense against the common pathogens S. aureus and C. albicans

Impacts of chronic kidney disease and albuminuria on associations between coronary heart disease and its traditional risk factors in type 2 diabetic patients – the Hong Kong diabetes registry

<p>Abstract</p> <p>Background</p> <p>Glycated haemoglobin (HbA_1c), blood pressure and body mass index (BMI) are risk factors for albuminuria, the latter in turn can lead to hyperlipidaemia. We used novel statistical analyses to examine how albuminuria and chronic kidney disease (CKD) may influence the effects of other risk factors on coronary heart disease (CHD).</p> <p>Methods</p> <p>A prospective cohort of 7067 Chinese type 2 diabetic patients without history of CHD enrolled since 1995 were censored on July 30^th, 2005. Cox proportional hazard regression with restricted cubic spline was used to auto-select predictors. Hazard ratio plots were used to examine the risk of CHD. Based on these plots, non-linear risk factors were categorised and the categorised variables were refitted into various Cox models in a stepwise manner to confirm the findings.</p> <p>Results</p> <p>Age, male gender, duration of diabetes, spot urinary albumin: creatinine ratio, estimated glomerular filtration rate, total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and current smoking status were risk factors of CHD. Linear association between TC and CHD was observed only in patients with albuminuria. Although in general, increased HDL-C was associated with decreased risk of CHD, full-range HDL-C was associated with CHD in an A-shaped manner with a zenith at 1.1 mmol/L. Albuminuria and CKD were the main contributors for the paradoxically positive association between HDL-C and CHD for HDL-C values less than 1.1 mmol/L.</p> <p>Conclusion</p> <p>In type 2 diabetes, albuminuria plays a linking role between conventional risk factors and CHD. The onset of CKD changes risk associations between lipids and CHD.</p

Enabling precision medicine in neonatology, an integrated repository for preterm birth research.

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research