Experience with model-based performance, reliability, and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture.     

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.     

Proteomics identification of differentially expressed proteins in the muscle of dysferlin myopathy patients

The muscular dystrophies are a large and heterogeneous group of neuromuscular disorders that can be classified according to the mode of inheritance, the clinical phenotype and the molecular defect. To better understand the pathological mechanisms of dysferlin myopathy we compared the protein-expression pattern in the muscle biopsies of six patients with this disease with six patients with limb girdle muscular dystrophy 2A, five with facioscapulohumeral dystrophy and six normal control subjects. To investigate differences in the expression levels of skeletal muscle proteins we used 2-DE and MS. Western blot or immunohistochemistry confirmed relevant results. The study showed specific increase expression of proteins involved in fast-to-slow fiber type conversion (ankyrin repeat protein 2), type I predominance (phosphorylated forms of slow troponin T), sarcomere stabilization (actinin-associated LIM protein), protein ubiquitination (TRIM 72) and skeletal muscle differentiation (Rho-GDP-dissociation inhibitor ly-GDI) in dysferlin myopathy. As anticipated, we also found differential expression of proteins common to all the muscular dystrophies studied. This comparative proteomic analysis suggests that in dysferlin myopathy (i) the type I fiber predominance is an active process of fiber type conversion rather than a selective loss of type II fibers and (ii) the dysregulation of proteins involved in muscle differentiation further confirms the role of dysferlin in this process.     

Relaxation of the mouse pubic symphysis during late pregnancy is not accompanied by the influx of granulocytes

In some animals, such as mice and guinea pigs, a hormonally controlled mechanism increases the flexibility of the pubic symphysis and enhances the cervical remodeling necessary for safe delivery. Cervical ripening during pregnancy is associated with a paradoxical influx of leukocytes. However, the changes in cell metabolism during relaxation of the mouse pubic symphysis for delivery have not been extensively studied. In this work, we used light microscopy and transmission and scanning electron microcopy, as well as immunohistochemistry and Western blotting for MMP-8, to investigate the involvement of granulocytes or resident stromal cells in the relaxation of the virgin pubic symphysis during late pregnancy (days 18 and 19, before delivery) in vivo and in explanted joints. MMP-8 was studied because this collagenase is a hallmark for cervical ripening associated with the influx of granulocytes during late pregnancy. Extensive dissolution and disorganization of the extracellular matrix was seen around fibroblastic-like cells in late pregnancy. In contrast to the cervix (positive control), morphological and immunohistochemical analyses revealed that there was no characteristic cellular inflammatory response in the interpubic tissue. Staining for MMP-8 was observed in chondroid and fibroblastic-like cells of virgin and relaxed interpubic ligament, respectively. However, no granulocytes were seen during the extensive remodeling of the pubic joint in late pregnancy. These results indicate that constitutive stromal cells may have an important role in tissue relaxation during remodeling of the pubic symphysis in pregnancy.     

Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease

Magnetic Resonance Materials in Physics, Biology and Medicine - Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small...     

GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure

Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art.

     

Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

Scavenging of reactive oxygen and nitrogen species with nanomaterials

Reactive oxygen and nitrogen species (RONS) are essential for normal physiological processes and play important roles in cell signaling, immunity, and tissue homeostasis. However, excess radical species are implicated in the development and augmented pathogenesis of various diseases. Several antioxidants may restore the chemical balance, but their use is limited by disappointing results of clinical trials. Nanoparticles are an attractive therapeutic alternative because they can change the biodistribution profile of antioxidants, and possess intrinsic ability to scavenge RONS. Herein, we review the types of RONS, how they are implicated in several diseases, and the types of nanoparticles with inherent antioxidant capability, their mechanisms of action, and their biological applications.     

Absolute Emission Spectra from Bacillus subtilis and Escherichia coli Vegetative Cells in Solution

Spectrally resolved emission (270-560 nm) from dilute suspensions of washed Bacillus subtilis and Escherichia coli were measured by use of tunable laser excitation between 270 and 300 nm. Integrated absolute emission cross sections increase with decreasing excitation wavelength and range from 1.8 x 10(-12) to 6.0 x 10(-11) cm(2)/(particle sr). An emission band near 340 nm dominates all observed spectra. At each excitation wavelength spectrally resolved emissions from the E. coli and B. subtilis suspensions are indistinguishable.