Application of wearable sensors for human gait analysis using fuzzy computational algorithm

The authors have developed and tested a wearable inertial sensor system for the acquisition of gait features. The sensors were placed on anatomical segments of the lower limb: foot, shank, thigh, and hip, and the motion data were then captured in conjunction with 3D ground reaction forces (GRFs). The method of relational matrix was applied to develop a rule-based system, an intelligent fuzzy computational algorithm. The rule-based system provides a feature matrix model representing the strength of association or interaction amongst the elements of the gait functions (limb-segments accelerations and GRFs) throughout the gait cycle. A comparison between the reference rule-based data and an input test data was evaluated using a fuzzy similarity algorithm. This system was tested and evaluated using two subject groups: 10 healthy subjects were recruited to establish the reference fuzzy rule-base, and 4 relapsing remitting multiple sclerosis subjects were used as an input test data; and the grade of similarity between them was evaluated. This similarity provides a quantitative assessment of mobility state of the impaired subject. This algorithmic tool may be helpful to the clinician in the identification of pathological gait impairments, prescribe treatment, and assess the improvements in response to therapeutic intervention.     

An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications

Wireless sensor networks (WSNs) applications are growing rapidly in various fields such as environmental monitoring, health care management, and industry control. However, WSN's are characterized by constrained resources especially; energy which shortens their lifespan. One of the most important factors that cause a rapid drain of energy is radio communication of multivariate data between nodes and base station. Besides, the dynamic changes of environmental variables pose a need for an adaptive solution that cope with these changes over the time. In this paper, a new adaptive and efficient dimension reduction model (APCADR) is proposed for hierarchical sensor networks based on the candid covariance-free incremental PCA (CCIPCA). The performance of the model is evaluated using three real sensor networks datasets collected at Intel Berkeley Research Lab (IBRL), Great St. Bernard (GSB) area, and Lausanne Urban Canopy Experiments (LUCE). Experimental results show 33.33% and 50% reduction of multivariate data in dynamic and static environments, respectively. Results also show that 97–99% of original data is successfully approximated at cluster heads in both environment types. A comparison with the multivariate linear regression model (MLR) and simple linear regression model (SLR) shows the advantage of the proposed model in terms of efficiency, approximation accuracy, and adaptability with dynamic environmental changes.     

Extracting lexical and phrasal paraphrases: a review of the literature

Recent advances in natural language processing have increased the popularity of paraphrase extraction. Most of the attention, however, has been focused on the extraction methods only without taking the resource factor into the consideration. Unknowingly, there is a strong relationship between them and the resource factor also plays an equally important role in paraphrase extraction. In addition, almost all of the previous studies have been focused on corpus-based methods that extract paraphrases from corpora based solely on syntactic similarity. Despite the popularity of corpus-based methods, a considerable amount of research has consistently shown that these methods are vulnerable to several types of erroneous paraphrases. For these reasons, it is necessary to evaluate whether the trend is moving in a positive direction. This paper reviews the major research on paraphrase extraction methods in detail. It begins by exploring the definition of paraphrase from different perspectives to provide a better understanding of the concept of paraphrase extraction. It then studies the characteristics and potential uses of different types of paraphrase resources. After that, it divides paraphrase extraction methods into four main categories: heuristic-based, knowledge-based, corpus-based and hybrid-based and summarizes their strengths and weaknesses. This paper concludes with some potential open research issues for future directions.     

Infertility update: use of assisted reproductive technology

OBJECTIVE: To describe assisted reproductive technology (ART) and use of medications during these procedures. DATA SOURCES: Recent clinical literature. STUDY SELECTION: Not applicable. DATA EXTRACTION: Not applicable. DATA SYNTHESIS: ARTs are procedures used in treatment of infertility that involve removal of oocytes and their manipulation outside the woman's uterus. The simplest form of ART, in vitro fertilization, involves aspirating eggs from the ovaries, fertilizing them outside the body, and transferring the embryos into the uterus at the four- to eight-cell stage. Experimental regimens for in vitro fertilization include use of various medications (gonadotropin-releasing hormone agonists, human menotropins, follicle-stimulating hormone, growth hormone) at varying points in the menstrual cycle and after introduction of the embryo into the uterus. Human chorionic gonadotropin has been used to increase implantation of embryos during the woman's luteal phase. Gamete intrafallopian transfer (GIFT) involves transfer of oocytes and sperm into the fallopian tubes, where fertilization takes place. This technique has the advantage of causing the zygote to enter the uterus at the time it would during natural conception. Zygote intrafallopian transfer is similar to GIFT, except that fertilization occurs in vitro, with embryos placed in the fallopian tubes at the two-cell stage. Various micromanipulation techniques and innovative sperm aspiration procedures are currently under development. CONCLUSION: Many advancements have been made in ART, and pharmacists who understand these procedures can serve patients by providing medication information in an empathetic and supportive manner.     

IFN-alpha 2B enhances Th1 cytokine responses in bladder cancer patients receiving Mycobacterium bovis bacillus Calmette-Guérin immunotherapy

Combination therapy with intravesical bacillus Calmette-Guérin (BCG) plus IFN-alpha for superficial bladder cancer has been demonstrated to be more effective than either single agent alone in animal studies and of suggested greater efficacy in clinical studies. However, the mechanism by which IFN-alpha enhances BCG-mediated antitumor activity is poorly understood. Using PBMCs from bladder cancer patients, IFN-alpha was found to substantially enhance the efficacy of BCG to induce IFN-gamma production. Among 34 patients tested, 80% showed >4-fold increase. This effect of IFN-alpha was observed in both initial and memory responses to BCG. In addition, IFN-alpha up-regulated BCG-induced IL-12 and TNF-alpha and down-regulated BCG-induced IL-10. Neutralizing endogenous IL-10 or adding exogenous IL-12 provided further synergy for IFN-gamma production. In clinical practice, intravesical IFN-alpha 2B (50 million units (MU)/dose) was observed to accelerate urinary IFN-gamma production to low-dose BCG (one-tenth or one-third of a full dose) in patients treated with combination therapy compared with BCG alone. These results suggest that IFN-alpha is a potent BCG enhancer that polarizes the BCG-induced immune response toward the cellular immune pathway by promoting Th1 cytokine expression and reducing Th2 cytokine expression. This study provides an immunological basis for future rational use of IFN-alpha in conjunction with intravesical BCG for bladder cancer immunotherapy.     

Measurement of distributions of small-scale energy depositions from low-linear energy transfer particles using the superheated drop detector

We have developed a new detection method for measuring distributions of energy depositions from particles characterized by low linear energy transfers (LETs). In particular, we have developed a detection system to measure energy depositions produced by electrons and photons on nanometer scales. The detector is based upon the operational principles of the superheated drop detector (SDD). SDDs consist of tiny droplets of superheated liquid suspended within a gel-like emulsion. The SDDs in this study are fabricated using Freon-115 and a glycerol-based gel as the superheated liquid and host medium. This SDD configuration is operated as a threshold temperature-dependent detector for measuring nanoscopic distributions of energy depositions from low-LET particles. Measured results are compared to the calculated distributions of energy depositions along an electron track. A new electron track code, ESLOW3.1, is used to perform the computational study. Measurements show good agreement with computational results in the energy deposition range of 40-200 eV.