An Assessment Tool of Performance Based Logistics Appropriateness

Performance-Based Logistics (PBL) is the most preferred product support strategy in the Department of Defense. Using performance-based acquisition methods to the maximum extent practicable when acquiring services is mandated. But although PBL should be used whenever feasible, few documents show how to measure its feasibility. The aim of this research is to fill this gap by answering the following question: What factors determine the appropriateness of the selection of PBL as a strategy for a specific acquisition? This study seeks to determine which factors affect success when selecting PBL as an acquisition method. Each factor is examined in detail and then built into a spreadsheet tool which helps to assess the appropriateness of PBL as an acquisition method. The purpose of the tool is to aid in PBL-related decision making processes and business case analyses. The questions asked by the tool will help the user make a more objective assessment in a relatively short period of time

Comparison of fear in children with and without mental retardation : a study from Turkey

Fear, which is present right from the very early periods of human life is one of the most common forms of emotion. Intelligence is also suggested to be among the major factors that affect fear. This study was designed and conducted to examine the fears of trainable mentally retarded children and those without mental retardation. Eighty-eight trainable mentally retarded childern aged 10- 15 years and 122 children without mental retardation aged between 7-15 years were included in the study. The "Fear Survey for Children with and without Mental Retardation" developed by Ramirez and Kratochwill was used as the data collection tool. After statistical analysis, a significant relationship was found between mental retardation and the healthy states in terms of both the number of fear episodes and in its severity.peer-reviewe

Combining in silico docking and molecular dynamics simulations to predict the impact of mutations on the substrate specificity of BTL2 lipase

Lipases are enzymes that hydrolyze the ester bond between acyl groups and glycerol in triacylglycerides which gives the products of glycerol and fatty acids. Bacillus thermocatenulatus lipase (BTL2) has shown highest activity toward tributyrin (C4) as substrate. While broad selectivity on the chain length of the fatty acids has a key role in waste water treatment, and laundry formulations; short chain length specificity can be used in the food and cosmetic industry. In order to predict its chain length substrate specificity (tributyrin (C4)/tricaprylin (C8)) upon mutation, we developed a scoring function which combines in silico docking and molecular dynamics tools. After calibration on experimentally validated mutants, our scoring function is able to discriminate substrates specificities and predict the impact of a mutation (whether it enhances or reduces) in a rapid and accurate manner (overall correlation r=0.7930, p=0.0007). Also ranking of substrate specificities within the mutants were 100% correct. This method can be powerfully adapted to other protein families to predict the effect of a mutation for the one specific substrate or multiple substrates

Design of a deep learning based nonlinear aerodynamic surrogate model for UAVs

In this paper, we present a deep learning based surrogate model to determine non-linear aerodynamic characteristics of UAVs. The main advantage of this model is that it can predict the aerodynamic properties of the configurations very quickly by using only geometric configuration parameters without the need for any special input data or pre-process phase. This provides a crucial and explicit design and synthesis tool for mini and small UAVs. To achieve this goal, a large data set, which includes thousands of wing-tail configurations geometry parameters and performance coefficients, was generated using the previously developed and computationally very efficient non-linear lifting line method. This data is used for training the artificial neural network model. The preliminary results show that the neural network model has generalization capability. The aerodynamic model predictions show almost 1-1 coincidence with the numerical data even for configurations with different 2D profiles that are not used in model training. Specifically, the results of test cases are found to capture both the linear and non-linear region of the lift curves, by predicting the maximum lift coefficient, the stall angle of attack, and the characteristics of post-stall region correctly. Similarly, total drag and pitching moment coefficients are predicted successfully. The developed methodology provides the basis for bidirectional design optimization and offers insight for an inverse tool that can calculate geometry parameters for a given design condition

DolphinNext: a distributed data processing platform for high throughput genomics

BACKGROUND: The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. RESULTS: To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis. CONCLUSION: DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results

Heavy metal bioaccumulation by the important food plant, olea europaea L., in an ancient metalliferous polluted area of Cyprus

Aspects of the bioaccumulation of heavy metals are reviewed and possible evidence of homeostasis is highlighted. Examination and analysis of olive (Olea europaea L.) trees growing in close proximity to a copper dominated spoil tip dating from at least 2000 years BP, on the island of Cyprus, revealed both bioaccumulation and partitioning of copper, lead and zinc in various parts of the tree. A factor to quantify the degree of accumulation is illustrated and a possible seed protective mechanism suggested

An atlas of cell types in the mouse epididymis and vas deferens

Following testicular spermatogenesis, mammalian sperm continue to mature in a long epithelial tube known as the epididymis, which plays key roles in remodeling sperm protein, lipid, and RNA composition. To understand the roles for the epididymis in reproductive biology, we generated a single-cell atlas of the murine epididymis and vas deferens. We recovered key epithelial cell types including principal cells, clear cells, and basal cells, along with associated support cells that include fibroblasts, smooth muscle, macrophages and other immune cells. Moreover, our data illuminate extensive regional specialization of principal cell populations across the length of the epididymis. In addition to region-specific specialization of principal cells, we find evidence for functionally specialized subpopulations of stromal cells, and, most notably, two distinct populations of clear cells. Our dataset extends on existing knowledge of epididymal biology, and provides a wealth of information on potential regulatory and signaling factors that bear future investigation

An improved zebrafish transcriptome annotation for sensitive and comprehensive detection of cell type-specific genes

The zebrafish is ideal for studying embryogenesis and is increasingly applied to model human disease. In these contexts, RNA-sequencing (RNA-seq) provides mechanistic insights by identifying transcriptome changes between experimental conditions. Application of RNA-seq relies on accurate transcript annotation for a genome of interest. Here, we find discrepancies in analysis from RNA-seq datasets quantified using Ensembl and RefSeq zebrafish annotations. These issues were due, in part, to variably annotated 3\u27 untranslated regions and thousands of gene models missing from each annotation. Since these discrepancies could compromise downstream analyses and biological reproducibility, we built a more comprehensive zebrafish transcriptome annotation that addresses these deficiencies. Our annotation improves detection of cell type-specific genes in both bulk and single cell RNA-seq datasets, where it also improves resolution of cell clustering. Thus, we demonstrate that our new transcriptome annotation can outperform existing annotations, providing an important resource for zebrafish researchers

A new nonlinear lifting-line method for aerodynamic analysis and deep learning modeling of small UAVs

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles (UAVs). First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and poststall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics (CFD). This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of UAVs. The major novel feature of this model is that it can predict the aerodynamic properties of UAV configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of a UAV over a wide angle of attack range on the order of milliseconds, whereas CFD solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example