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Aircraft control and simulation : dynamics, controls design, and autonomous systems
This third edition is a comprehensive guide to aircraft control and simulation. The updated text covers flight control systems, flight dynamics, aircraft modelling, and flight simulation from both classical design and modern perspectives, as well as two new chapters on the modelling, simulation, and adaptive control of unmanned aerial vehicles.
Book
Blood pressure variability and cardiovascular disease: systematic review and meta-analysis
Objective To systematically review studies quantifying the associations of long term (clinic), mid-term (home), and short term (ambulatory) variability in blood pressure, independent of mean blood pressure, with cardiovascular disease events and mortality.Data sources Medline, Embase, Cinahl, and Web of Science, searched to 15 February 2016 for full text articles in English.Eligibility criteria for study selection Prospective cohort studies or clinical trials in adults, except those in patients receiving haemodialysis, where the condition may directly impact blood pressure variability. Standardised hazard ratios were extracted and, if there was little risk of confounding, combined using random effects meta-analysis in main analyses. Outcomes included all cause and cardiovascular disease mortality and cardiovascular disease events. Measures of variability included standard deviation, coefficient of variation, variation independent of mean, and average real variability, but not night dipping or day-night variation.Results 41 papers representing 19 observational cohort studies and 17 clinical trial cohorts, comprising 46 separate analyses were identified. Long term variability in blood pressure was studied in 24 papers, mid-term in four, and short-term in 15 (two studied both long term and short term variability). Results from 23 analyses were excluded from main analyses owing to high risks of confounding. Increased long term variability in systolic blood pressure was associated with risk of all cause mortality (hazard ratio 1.15, 95% confidence interval 1.09 to 1.22), cardiovascular disease mortality (1.18, 1.09 to 1.28), cardiovascular disease events (1.18, 1.07 to 1.30), coronary heart disease (1.10, 1.04 to 1.16), and stroke (1.15, 1.04 to 1.27). Increased mid-term and short term variability in daytime systolic blood pressure were also associated with all cause mortality (1.15, 1.06 to 1.26 and 1.10, 1.04 to 1.16, respectively).Conclusions Long term variability in blood pressure is associated with cardiovascular and mortality outcomes, over and above the effect of mean blood pressure. Associations are similar in magnitude to those of cholesterol measures with cardiovascular disease. Limited data for mid-term and short term variability showed similar associations. Future work should focus on the clinical implications of assessment of variability in blood pressure and avoid the common confounding pitfalls observed to date.Systematic review registration PROSPERO CRD42014015695.
Journal Article
Himalayan megathrust geometry and relation to topography revealed by the Gorkha earthquake
The Himalayan mountain range has been the locus of some of the largest continental earthquakes, including the 2015 magnitude 7.8 Gorkha earthquake. Competing hypotheses suggest that Himalayan topography is sustained and plate convergence is accommodated either predominantly on the main plate boundary fault, or more broadly across multiple smaller thrust faults. Here we use geodetic measurements of surface displacement to show that the Gorkha earthquake ruptured the Main Himalayan Thrust fault. The earthquake generated about 1 m of uplift in the Kathmandu Basin, yet caused the high Himalaya farther north to subside by about 0.6 m. We use the geodetic data, combined with geologic, geomorphological and geophysical analyses, to constrain the geometry of the Main Himalayan Thrust in the Kathmandu area. Structural analyses together with interseismic and coseismic displacements are best explained by a steep, shallow thrust fault flattening at depth between 5 and 15 km and connecting to a mid-crustal, steeper thrust. We suggest that present-day convergence across the Himalaya is mostly accommodated by this fault—no significant motion on smaller thrust faults is required. Furthermore, given that the Gorkha earthquake caused the high Himalayan mountains to subside and that our fault geometry explains measured interseismic displacements, we propose that growth of Himalayan topography may largely occur during the ongoing post-seismic phase.
How Himalayan topography is built is unclear. Analysis of surface displacement during the 2015 Gorkha earthquake suggests that large earthquakes may lower the high Himalayan mountains, and topography may grow during the interseismic phase.
Journal Article
Practice Guidelines for the Diagnosis and Management of Skin and Soft Tissue Infections: 2014 Update by the Infectious Diseases Society of America
A panel of national experts was convened by the Infectious Diseases Society of America (IDSA) to update the 2005 guidelines for the treatment of skin and soft tissue infections (SSTIs). The panel's recommendations were developed to be concordant with the recently published IDSA guidelines for the treatment of methicillin-resistant Staphylococcus aureus infections. The focus of this guideline is the diagnosis and appropriate treatment of diverse SSTIs ranging from minor superficial infections to life-threatening infections such as necrotizing fasciitis. In addition, because of an increasing number of immunocompromised hosts worldwide, the guideline addresses the wide array of SSTIs that occur in this population. These guidelines emphasize the importance of clinical skills in promptly diagnosing SSTIs, identifying the pathogen, and administering effective treatments in a timely fashion.
Journal Article
Converting tabular data into images for deep learning with convolutional neural networks
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.
Journal Article
High-throughput generation, optimization and analysis of genome-scale metabolic models
Reconstructing a metabolic model from the genome sequence of an organism is a useful but arduous approach for predicting phenotypes. Henry
et al
. describe a resource that automates most of this process and apply it to create >100 new metabolic models of microbes.
Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking ∼48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.
Journal Article
Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations
Multiple models describe the formation and evolution of distinct microbial phylogenetic groups. These evolutionary models make different predictions regarding how adaptive alleles spread through populations and how genetic diversity is maintained. Processes predicted by competing evolutionary models, for example, genome-wide selective sweeps vs gene-specific sweeps, could be captured in natural populations using time-series metagenomics if the approach were applied over a sufficiently long time frame. Direct observations of either process would help resolve how distinct microbial groups evolve. Here, from a 9-year metagenomic study of a freshwater lake (2005–2013), we explore changes in single-nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in 30 bacterial populations. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied by >1000-fold among populations. SNP allele frequencies also changed dramatically over time within some populations. Interestingly, nearly all SNP variants were slowly purged over several years from one population of green sulfur bacteria, while at the same time multiple genes either swept through or were lost from this population. These patterns were consistent with a genome-wide selective sweep in progress, a process predicted by the ‘ecotype model’ of speciation but not previously observed in nature. In contrast, other populations contained large, SNP-free genomic regions that appear to have swept independently through the populations prior to the study without purging diversity elsewhere in the genome. Evidence for both genome-wide and gene-specific sweeps suggests that different models of bacterial speciation may apply to different populations coexisting in the same environment.
Journal Article
Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review
Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.
Journal Article
Predicting tumor cell line response to drug pairs with deep learning
Background
The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.
Results
We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity.
Conclusions
We present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.
Journal Article