MicroRNA-375 plays a dual role in prostate carcinogenesis

Background: Prostate cancer (PCa), a highly incident and heterogeneous malignancy, mostly affects men from developed countries. Increased knowledge of the biological mechanisms underlying PCa onset and progression are critical for improved clinical management. MicroRNAs (miRNAs) deregulation is common in human cancers, and understanding how it impacts in PCa is of major importance. MiRNAs are mostly downregulated in cancer, although some are overexpressed, playing a critical role in tumor initiation and progression. We aimed to identify miRNAs overexpressed in PCa and subsequently determine its impact in tumorigenesis. Results: MicroRNA expression profiling in primary PCa and morphological normal prostate (MNPT) tissues identified 17 miRNAs significantly overexpressed in PCa. Expression of three miRNAs, not previously associated with PCa, was subsequently assessed in large independent sets of primary tumors, in which miR-182 and miR-375 were validated, but not miR-32. Significantly higher expression levels of miR-375 were depicted in patients with higher Gleason score and more advanced pathological stage, as well as with regional lymph nodes metastases. Forced expression of miR-375 in PC-3 cells, which display the lowest miR-375 levels among PCa cell lines, increased apoptosis and reduced invasion ability and cell viability. Intriguingly, in 22Rv1 cells, which displayed the highest miR-375 expression, knockdown experiments also attenuated the malignant phenotype. Gene ontology analysis implicated miR-375 in several key pathways deregulated in PCa, including cell cycle and cell differentiation. Moreover, CCND2 was identified as putative miR-375 target in PCa, confirmed by luciferase assay. Conclusions: A dual role for miR-375 in prostate cancer progression is suggested, highlighting the importance of cellular context on microRNA targeting.

Evaluation of ultrasound, ultrasonophoresis and homeopathic mesotherapy on Cellulite treatment

Introduction: Cellulite is a complex architectural disorder with multifactorial etiologies that is prevalent in 98% of the women (1). Nowadays several aesthetic treatments are being used: surgical, cosmetic, physical, mechanical, and thermal. (2) Most treatments lack a substantial proof of efficacy. Objective: The purpose of this study was to test and evaluate the efficacy of Ultrasound, Homeopathic Ultrasonophoresis, and Homeopathic Mesotherapy versus control in cellulite in a population of women from ESTSP. Methods: Female volunteers (n=23), Caucasian, aged between 18-31 years, with BMI 19-27 kg/m2 with clinical cellulite gradation on the Cellulite Grading Scale of 1 to 4 were included in a control controlled study. Subjects were assigned in four different groups: Group I (Control, n=6), Group II (Ultrasound, (n=5), Group III (Homeopathic Ultrasonophoresis, n=6), Group IV (Homeopathic Mesotherapy, n=6). Groups II to IV were treated 3 times per week, for a total of 10 sessions. Cellulite gradation was evaluated at the beginning and the end of the trial by means of clinical photography, using a Canon IXUS 65 (6 mega pixels). For homeopathic treatments Dr. Reckeweg® Rekin® 59, 13 and 42 – Dietmed were used. The rating of perceived pain during Homeopathic Mesotherapy was evaluated by a visual analogic scale (VAS). The equipment Sonopuls 692, Enraf-Nonius was used for Ultrasound and Ultrasonophoresis treatments. Results:The higher number of participants with improvement in cellulite graduation occurred in group II (80%), followed group III (50%) and by group IV (33%). The group in which more changes in cellulite gradation occurred was group II, 20% of the individuals improved their score in 2 points. Results were statistically different between Group I and Group II, p=0,015. During the treatments of homeopathic mesotherapy the pain diminished 1 value in VAS scale. Discussion and Conclusion: Although all the three interventions groups were effective in the improvement of cellulite, as expected from previous works described in the literature, (2) only the ultrasound group was statistically different from control. These preliminary results point to the need of a new study using a higher number of participants and the same methodology.

Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

Heterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

A Green Approach for Selfish Misbehavior Detection in 802.11-Based Wireless Networks

IEEE 802.11 is one of the most well-established and widely used standard for wireless LAN. Its Medium Access control (MAC) layer assumes that the devices adhere to the standard’s rules and timers to assure fair access and sharing of the medium. However, wireless cards driver flexibility and configurability make it possible for selfish misbehaving nodes to take advantages over the other well-behaving nodes. The existence of selfish nodes degrades the QoS for the other devices in the network and may increase their energy consumption. In this paper we propose a green solution for selfish misbehavior detection in IEEE 802.11-based wireless networks. The proposed scheme works in two phases: Global phase which detects whether the network contains selfish nodes or not, and Local phase which identifies which node or nodes within the network are selfish. Usually, the network must be frequently examined for selfish nodes during its operation since any node may act selfishly. Our solution is green in the sense that it saves the network resources as it avoids wasting the nodes energy by examining all the individual nodes of being selfish when it is not necessary. The proposed detection algorithm is evaluated using extensive OPNET simulations. The results show that the Global network metric clearly indicates the existence of a selfish node while the Local nodes metric successfully identified the selfish node(s). We also provide mathematical analysis for the selfish misbehaving and derived formulas for the successful channel access probability.

A framework for memory contention analysis in multi-core platforms

The last decade has witnessed a major shift towards the deployment of embedded applications on multi-core platforms. However, real-time applications have not been able to fully benefit from this transition, as the computational gains offered by multi-cores are often offset by performance degradation due to shared resources, such as main memory. To efficiently use multi-core platforms for real-time systems, it is hence essential to tightly bound the interference when accessing shared resources. Although there has been much recent work in this area, a remaining key problem is to address the diversity of memory arbiters in the analysis to make it applicable to a wide range of systems. This work handles diverse arbiters by proposing a general framework to compute the maximum interference caused by the shared memory bus and its impact on the execution time of the tasks running on the cores, considering different bus arbiters. Our novel approach clearly demarcates the arbiter-dependent and independent stages in the analysis of these upper bounds. The arbiter-dependent phase takes the arbiter and the task memory-traffic pattern as inputs and produces a model of the availability of the bus to a given task. Then, based on the availability of the bus, the arbiter-independent phase determines the worst-case request-release scenario that maximizes the interference experienced by the tasks due to the contention for the bus. We show that the framework addresses the diversity problem by applying it to a memory bus shared by a fixed-priority arbiter, a time-division multiplexing (TDM) arbiter, and an unspecified work-conserving arbiter using applications from the MediaBench test suite. We also experimentally evaluate the quality of the analysis by comparison with a state-of-the-art TDM analysis approach and consistently showing a considerable reduction in maximum interference.

Microcystin-LR detection in water by the Fabry–Pérot interferometer using an optical fibre coated with a sol–gel imprinted sensing membrane

Cyanobacteria deteriorate the water quality and are responsible for emerging outbreaks and epidemics causing harmful diseases in Humans and animals because of their toxins. Microcystin-LR (MCT) is one of the most relevant cyanotoxin, being the most widely studied hepatotoxin. For safety purposes, the World Health Organization recommends a maximum value of 1 μg L−1 of MCT in drinking water. Therefore, there is a great demand for remote and real-time sensing techniques to detect and quantify MCT. In this work a Fabry–Pérot sensing probe based on an optical fibre tip coated with a MCT selective thin film is presented. The membranes were developed by imprinting MCT in a sol–gel matrix that was applied over the tip of the fibre by dip coating. The imprinting effect was obtained by curing the sol–gel membrane, prepared with (3-aminopropyl) trimethoxysilane (APTMS), diphenyl-dimethoxysilane (DPDMS), tetraethoxysilane (TEOS), in the presence of MCT. The imprinting effect was tested by preparing a similar membrane without template. In general, the fibre Fabry–Pérot with a Molecular Imprinted Polymer (MIP) sensor showed low thermal effect, thus avoiding the need of temperature control in field applications. It presented a linear response to MCT concentration within 0.3–1.4 μg L−1 with a sensitivity of −12.4 ± 0.7 nm L μg−1. The corresponding Non-Imprinted Polymer (NIP) displayed linear behaviour for the same MCT concentration range, but with much less sensitivity, of −5.9 ± 0.2 nm L μg−1. The method shows excellent selectivity for MCT against other species co-existing with the analyte in environmental waters. It was successfully applied to the determination of MCT in contaminated samples. The main advantages of the proposed optical sensor include high sensitivity and specificity, low-cost, robustness, easy preparation and preservation.

Nonlinear Dynamics for Local Fractional Burgers Equation Arising in Fractal Flow

The local fractional Burgers’ equation (LFBE) is investigated from the point of view of local fractional conservation laws envisaging a nonlinear local fractional transport equation with a linear non-differentiable diffusion term. The local fractional derivative transformations and the LFBE conversion to a linear local fractional diffusion equation are analyzed.

A Review of Operational Matrices and Spectral Techniques for Fractional Calculus

Recently, operational matrices were adapted for solving several kinds of fractional differential equations (FDEs). The use of numerical techniques in conjunction with operational matrices of some orthogonal polynomials, for the solution of FDEs on finite and infinite intervals, produced highly accurate solutions for such equations. This article discusses spectral techniques based on operational matrices of fractional derivatives and integrals for solving several kinds of linear and nonlinear FDEs. More precisely, we present the operational matrices of fractional derivatives and integrals, for several polynomials on bounded domains, such as the Legendre, Chebyshev, Jacobi and Bernstein polynomials, and we use them with different spectral techniques for solving the aforementioned equations on bounded domains. The operational matrices of fractional derivatives and integrals are also presented for orthogonal Laguerre and modified generalized Laguerre polynomials, and their use with numerical techniques for solving FDEs on a semi-infinite interval is discussed. Several examples are presented to illustrate the numerical and theoretical properties of various spectral techniques for solving FDEs on finite and semi-infinite intervals.

Prevalence of Asthma in a Portuguese Countryside Town:Repercussions on Absenteeism and Self-Concept

Background: Childhood asthma represents an increasing health problem and is the leading cause of hospital admission and absenteeism in children with chronic disease. It also compromises quality of life, eventually contributing to disturbances in self-concept. Self-concept is a recent and global perspective of “the self” and relates to skills, self-image and self-esteem. Little information is available on this topic and there are no data from Portuguese countryside towns. Objective: The aim of this study was to determine the prevalence of asthma among all school children in the 5th and 6th grades in a Portuguese countryside town and to establish its possible correlation with absenteeism and self-concept. Methods: In April 2002, two questionnaires were administered in the presence of the researcher to a group of 950 children attending different schools. The children completed the internationally renowned questionnaires: ISAAC and the Self-Concept Scale by Susan Harter. Results: Our sample (n = 818) had a mean age of 11 years (10-15 years) and a male-to-female ratio of 1/1. The cumulative prevalence of asthma was11.9% and that of active asthma was 8.8 %; 63.9% of asthmatics were male and 36.1 % were female. The mean age of asthmatics was 11.34 years and 74 % had active symptoms. Comparison of this group of 97 asthmatic children with the remaining children revealed a statistically significant correlation between the presence of asthma and school absenteeism (global: p = 0.04; gymnastics: 0.05). Regarding the Self-Concept Scale a statistically significant association was found between the presence of asthma and school achievement (p = 0.027), physical appearance (p = 0.015), behavior (p < 0.000) and self-esteem (p < 0.000). No statistically significant correlations were found in social acceptance (p = 0.289) or athletic competence (p = 0.085). Asthmatic boys had higher self-concept scores than girls, except in the domain of behavior. Conclusions: Twelve percent of the population studied was asthmatic. In asthmatic children, absenteeism was higher and self-concept was lower for almost all domains, except social acceptance and athletic achievement, probably due to overprotection.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Antifungal susceptibilities of clinical and environmental isolates of Cryptococcus neoformans in goiânia city, Goiás, Brasil

We evaluated the antifungal activities of amphotericin B, fluconazole, itraconazole and voriconazole in 70 Cryptococcus neoformans strains obtained from cerebrospinal fluid from AIDS patients and 40 C. neoformans strains isolated from the environment. Four clinical isolates were identified as C. neoformans var. gattii. The susceptibility test was done using a broth microdilution method according to NCCLS M27-A2. Range minimal inhibitory concentrations (MICs) for C. neoformans clinical isolates were 0.06-1.0 µg/mL for amphotericin B, 0.125-8 µg/mL for fluconazole, 0.03-0.5 µg/mL for itraconazole and 0.03-0.25 µg/mL for voriconazole. C. neoformans environmental isolates showed range MICs 0.015-0.125 µg/mL, 0.25-2.0 µg/mL, 0.007-0.125 µg/mL and 0.03-0.25 µg/mL for amphotericin B, fluconazole, itraconazole and voriconazole respectively. The MICs results obtained from clinical and environmental isolates showed similar pattern of susceptibility and no resistance has been found in our isolates.

Prevalence and associated risk factors for Giardia lamblia infection among children hospitalized for diarrhea in Goiânia, Goiás State, Brazil

The objective of this study was to determine the prevalence and to identify risk factors associated with Giardia lamblia infection in diarrheic children hospitalized for diarrhea in Goiânia, State of Goiás, Brazil. A cross-sectional study was conducted and a comprehensive questionnaire was administered to the child's primary custodian. Fixed effects logistic regression was used to determine the association between infection status for G. lamblia and host, sociodemographic, environmental and zoonotic risk factors. A total of 445 fecal samples were collected and processed by the DFA methodology, and G. lamblia cysts were present in the feces of 44 diarrheic children (9.9%). A variety of factors were found to be associated with giardiasis in these population: age of children (OR, 1.18; 90% CI, 1.0 - 1.36; p = 0.052), number of children in the household (OR 1.45; 90% CI, 1.13 - 1.86; p = 0.015), number of cats in the household (OR, 1.26; 90% CI, 1.03 -1.53; p = 0.059), food hygiene (OR, 2.9; 90% CI, 1.34 - 6.43; p = 0.024), day-care centers attendance (OR, 2.3; 90% CI, 1.20 - 4.36; p = 0.034), living on a rural farm within the past six months prior hospitalization (OR, 5.4; CI 90%, 1.5 - 20.1; p = 0.03) and the number of household adults (OR, 0.59; 90% CI, 0.42 - 0.83; p = 0.012). Such factors appropriately managed may help to reduce the annual incidence of this protozoal infection in the studied population.

Predictivity Strength of the Spatial Variability of Phenanthrene Sorption Across Two Sandy Loam Fields

Sorption is commonly agreed to be the major process underlying the transport and fate of polycyclic aromatic hydrocarbons (PAHs) in soils. However, there is still a scarcity of studies focusing on spatial variability at the field scale in particular. In order to investigate the variation in the field of phenanthrene sorption, bulk topsoil samples were taken in a 15 × 15-m grid from the plough layer in two sandy loam fields with different texture and organic carbon (OC) contents (140 samples in total). Batch experiments were performed using the adsorption method. Values for the partition coefficient K d (L kg−1) and the organic carbon partition coefficient K OC (L kg−1) agreed with the most frequently used models for PAH partitioning, as OC revealed a higher affinity for sorption. More complex models using different OC compartments, such as non-complexed organic carbon (NCOC) and complexed organic carbon (COC) separately, performed better than single K OC models, particularly for a subset including samples with Dexter n < 10 and OC <0.04 kg kg−1. The selected threshold revealed that K OC-based models proved to be applicable for more organic fields, while two-component models proved to be more accurate for the prediction of K d and retardation factor (R) for less organic soils. Moreover, OC did not fully reflect the changes in phenanthrene retardation in the field with lower OC content (Faardrup). Bulk density and available water content influenced the phenanthrene transport mechanism phenomenon.

Detection of the peanut allergen Ara h 6 in foodstuffs using a voltammetric biosensing approach

A voltammetric biosensor for Ara h 6 (a peanut allergen) detection in food samples was developed. Gold nanoparticle-modified screen-printed carbon electrodes were used to develop a sandwich-type immunoassay using two-monoclonal antibodies. The antibody-antigen interaction was detected through the electrochemical detection of enzymatically deposited silver. The immunosensor presented a linear range between 1 and 100 ng/ml, as well as high precision (inter-day RSD ≤9.8 %) and accuracy (recoveries ≥96.7 %). The detection and quantification limits were 0.27 and 0.88 ng/ml, respectively. It was possible to detect small levels of Ara h 6 in complex food matrices.