A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.     

Beam search algorithms for the single machine total weighted tardiness scheduling problem with sequence-dependent setups

In this paper, we consider the single machine weighted tardiness scheduling problem with sequence-dependent setups. We present heuristic algorithms based on the beam search technique. These algorithms include classic beam search procedures, as well as the filtered and recovering variants. Previous beam search implementations use fixed beam and filter widths. We consider the usual fixed width algorithms, and develop new versions that use variable beam and filter widths.     

New insights into the morphology of Trypanosoma cruzi reservosome

Reservosomes are late endosomes present only in members of the Schizotrypanum subgenus of the Trypanosoma genus and are defined as the site of storage of endocytosed macromolecules and lysosomal enzymes. They have been extensively described in Trypanosoma cruzi epimastigote: are bounded by a membrane unit, present an electron-dense protein matrix with electron-lucent lipid inclusions, being devoid of inner membranes. Here we performed a detailed ultrastructural analysis of these organelles using a variety of electron microscopy techniques, including ultrathin sectioning, uranyl acetate stained preparations, and freeze fracture, either in intact epimastigotes or in isolated reservosomes. New informations were obtained. First, both isolated and in situ reservosomes presented small profiles of inner membranes that are morphologically similar to the membrane surrounding the organelle. In uranyl acetate stained preparations, internal membrane profiles turned out to be longer than they appeared in ultrathin section images and traversed the organelle diameter. Internal vesicles were also found. Second, endocytosed cargo are not associated with internal vesicles and reach reservosomes on board of vesicles that fuse with the boundary membrane, delivering cargo directly into reservosome lumen. Third, electron-lucent bodies with saturated lipid core surrounded by a membrane monolayer and with unusual rectangular shape were also observed. Fourth, it was possible to demonstrate the presence of intramembranous particles on the E face of both internal vesicles and the surrounding membrane. Collectively, these results indicate that reservosomes have a complex internal structure, which may correlate with their multiple functions.     

Estimating vulnerability to risks: an application in a biofuel supply chain

In the present work, we propose a theoretical model to identify and prioritize risks involved in a biofuel supply chain. We adopt a set of indicators associated with determinant factors of the supply chain to identify risks that are characterized through a risk matrix. We consider the five largest world biodiesel producers and included China due to its global market importance and potential impacts of its growth on the environment and society. To determine the impacts and the probability of occurrence of risks, we use the Canberra distance, as metrics. To facilitate the analysis and interpretation, a convenient manner is to express the results in terms of matrices. To exemplify the potentiality of the scheme and for the sake of simplicity, a more comprehensive discussion is focused on the Brazilian case, restricted to the Technology and Innovation, and Integration, Logistics and Infrastructure determining factors (dimensions) of the biodiesel supply chain. Concerning these determining factors, the Brazilian biodiesel chain shows strong vulnerability when compared with developed and developing countries, despite that the evolution of the data over recent years indicates small improvements in Integration, Logistics and Infrastructure dimension. Although in this work the calculations are restricted to the Canberra distance, the present approach may be applied to other distances to compare or validate the results. This work presents a contribution to model vulnerability to risks, providing to policy makers and stakeholders a tool to design, analyze and improve sustainability system by measuring its risks. The study of the contribution of each indicator suggests corrections to be taken and which indicators should be prioritized.     

Sorafenib delivery nanoplatform based on superparamagnetic iron oxide nanoparticles magnetically targets hepatocellular carcinoma

Currently,sorafenib is the only systemic therapy capable of increasing overall survival of hepatocellular carcinoma patients.Unfortunately,its side effects,particularly its overall toxicity,limit the therapeutic response that can be achieved.Superparamagnetic iron oxide nanoparticles (SPIONs) are very attractive for drug delivery because they can be targeted to specific sites in the body through application of a magnetic field,thus improving intratumoral accumulation and reducing adverse effects.Here,nanoformulations based on polyethylene glycol modified phospholipid micelles,loaded with both SPIONs and sorafenib,were successfully prepared and thoroughly investigated by complementary techniques.This nanovector system provided effective drug delivery,had an average hydrodynamic diameter of about 125 nm,had good stability in aqueous medium,and allowed controlled drug loading.Magnetic analysis allowed accurate determination of the amount of SPIONs embedded in each micelle.An in vitro system was designed to test whether the SPION micelles can be efficiently held using a magnetic field under typical flow conditions found in the human liver.Human hepatocellular carcinoma (HepG2) cells were selected as an in vitro system to evaluate tumor cell targeting efficacy of the superparamagnetic micelles loaded with sorafenib.These experiments demonstrated that this delivery platform is able to enhance sorafenib's antitumor effectiveness by magnetic targeting.The magnetic nanovectors described here represent promising candidates for targeting specific hepatic tumor sites,where selective release of sorafenib can improve its efficacy and safety profile.     

Identification of anovulation and transient luteal function using a urinary pregnanediol-3-glucuronide ratio algorithm

The sensitivity and specificity of a urinary pregnanediol-3-glucuronide (PdG) ratio algorithm to identify anovulatory cycles was studied prospectively in two independent populations of women. Urinary hormone data from the first group was used to develop the algorithm, and data from the second group was used for its validation. PdG ratios were calculated by a cycles method in which daily PdG concentrations indexed by creatinine (CR) from cycle day 11 onward were divided by a baseline PdG (average PdG/Cr concentration for cycle days 6-10). In the interval method, daily PdG/CR concentrations from day 1 onward were divided by baseline PdG (lowest 5-day average of PdG/CR values throughout the collection period). Evaluation of the first study population (n = 6) resulted in cycles with PdG ratios > or = 3 for > or = 3 consecutive days being classified as ovulatory; otherwise they were anovulatory. The sensitivity and specificity of the PdG ratio algorithm to identify anovulatory cycles in the second population were 75% and 89.5%, respectively, for all cycles (n = 88); 50% and 88.3% for first cycles (n = 40) using the cycles method; 75% and 92.2%, respectively, for all cycles (n = 89); and 50% and 94.1% for first cycles (n = 40) using the interval method. The "gold standard" for anovulation was weekly serum samples < or = 2 ng/ml progesterone. The sensitivity values for all cycles and for the first cycle using both methods were underestimated because of apparent misclassification of cycles using serum progesterone due to infrequent blood collection. Blood collection more than once a week would have greatly improved the sensitivity and modestly improved the specificity of the algorithm. The PdG ratio algorithm provides an efficient approach for screening urine samples collected in epidemiologic studies of reproductive health in women.     

A new discretized Lyapunov–Krasovskii functional for stability analysis and control design of time‐delayed TS fuzzy systems

This paper proposes a new Lyapunov–Krasovskii functional to cope with stability analysis and control design for time‐delay nonlinear systems modeled in the Takagi–Sugeno (TS) fuzzy form. The delay‐dependent conditions are formulated as linear matrix inequalities (LMIs), solvable through several numerical tools. By using the Gu's discretization technique and by employing an appropriated fuzzy functional, less conservative conditions are obtained. Numerical results illustrate the efficiency of the proposed methods. Copyright © 2010 John Wiley & Sons, Ltd.     

Reducing conservativeness in recent stability conditions of TS fuzzy systems

In this correspondence a new simple strategy for reducing the conservativeness in stability analysis of continuous-time Takagi-Sugeno fuzzy systems based on fuzzy Lyapunov functions is proposed. This new strategy generalizes previous results.     

A modular approach to learning manipulation strategies from human demonstration

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.     

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.