Bias dependent photocurrent collection in p-i-n a-Si : EVSiC : H heterojunction

A series of large area single layers and heterojunction cells in the assembly glass/ZnO:Al/p (SixC1-x:H)/i (Si:H)/n (SixC1-x:H)/Al (0

Bias-dependent photocurrent collection in p-i-n a-Si : H/SiC : H heterojunction

A series of large area single layers and glass/ZnO:AVp(SixC1-x:H)/i(Si:H)/n(SixC1-x:H)/AI (0 < x < 1) heterojunction cells were produced by plasma-enhanced chemical vapour deposition (PE-CVD) at low temperature. Junction properties, carrier transport and photogeneration are investigated from dark and illuminated current-voltage (J-V) and capacitance-voltage (C-V) characteristics. For the heterojunction cells atypical J-V characteristics under different illumination conditions are observed leading to poor fill factors. High series resistances around 106 Q are also measured. These experimental results were used as a basis for the numerical simulation of the energy band diagram, and the electrical field distribution of the structures. Further comparison with the sensor performance gave satisfactory agreement. Results show that the conduction band offset is the most limiting parameter for the optimal collection of the photogenerated carriers. As the optical gap increases and the conductivity of the doped layers decreases, the transport mechanism changes from a drift to a diffusion-limited process.

Re-entrant phase behaviour of network fluids: A patchy particle model with temperature-dependent valence

We study a model consisting of particles with dissimilar bonding sites ("patches"), which exhibits self-assembly into chains connected by Y-junctions, and investigate its phase behaviour by both simulations and theory. We show that, as the energy cost epsilon(j) of forming Y-junctions increases, the extent of the liquid-vapour coexistence region at lower temperatures and densities is reduced. The phase diagram thus acquires a characteristic "pinched" shape in which the liquid branch density decreases as the temperature is lowered. To our knowledge, this is the first model in which the predicted topological phase transition between a fluid composed of short chains and a fluid rich in Y-junctions is actually observed. Above a certain threshold for epsilon(j), condensation ceases to exist because the entropy gain of forming Y-junctions can no longer offset their energy cost. We also show that the properties of these phase diagrams can be understood in terms of a temperature-dependent effective valence of the patchy particles. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3605703]

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Dependent component analysis: a hyperspectral unmixing algorithm

Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.

Optimising and adapting the QoS of a dynamic set of inter-dependent tasks

Due to the growing complexity and adaptability requirements of real-time systems, which often exhibit unrestricted Quality of Service (QoS) inter-dependencies among supported services and user-imposed quality constraints, it is increasingly difficult to optimise the level of service of a dynamic task set within an useful and bounded time. This is even more difficult when intending to benefit from the full potential of an open distributed cooperating environment, where service characteristics are not known beforehand and tasks may be inter-dependent. This paper focuses on optimising a dynamic local set of inter-dependent tasks that can be executed at varying levels of QoS to achieve an efficient resource usage that is constantly adapted to the specific constraints of devices and users, nature of executing tasks and dynamically changing system conditions. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves as the algorithms are given more time to run, with a minimum overhead when compared against their traditional versions.

Dynamic QoS adaptation of inter- dependent task sets in cooperative embedded systems

Due to the growing complexity and dynamism of many embedded application domains (including consumer electronics, robotics, automotive and telecommunications), it is increasingly difficult to react to load variations and adapt the system's performance in a controlled fashion within an useful and bounded time. This is particularly noticeable when intending to benefit from the full potential of an open distributed cooperating environment, where service characteristics are not known beforehand and tasks may exhibit unrestricted QoS inter-dependencies. This paper proposes a novel anytime adaptive QoS control policy in which the online search for the best set of QoS levels is combined with each user's personal preferences on their services' adaptation behaviour. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves as the algorithms are given more time to run, with a minimum overhead when compared against their traditional versions.

Inhibition of Aspergillus fumigatus and its biofilm by Pseudomonas aeruginosa is dependent on the source, phenotype and growth conditions of the bacterium

Aspergillus fumigatus (Af) and Pseudomonas aeruginosa (Pa) are leading fungal and bacterial pathogens, respectively, in many clinical situations. Relevant to this, their interface and co-existence has been studied. In some experiments in vitro, Pa products have been defined that are inhibitory to Af. In some clinical situations, both can be biofilm producers, and biofilm could alter their physiology and affect their interaction. That may be most relevant to airways in cystic fibrosis (CF), where both are often prominent residents. We have studied clinical Pa isolates from several sources for their effects on Af, including testing involving their biofilms. We show that the described inhibition of Af is related to the source and phenotype of the Pa isolate. Pa cells inhibited the growth and formation of Af biofilm from conidia, with CF isolates more inhibitory than non-CF isolates, and non-mucoid CF isolates most inhibitory. Inhibition did not require live Pa contact, as culture filtrates were also inhibitory, and again non-mucoid>mucoid CF>non-CF. Preformed Af biofilm was more resistant to Pa, and inhibition that occurred could be reproduced with filtrates. Inhibition of Af biofilm appears also dependent on bacterial growth conditions; filtrates from Pa grown as biofilm were more inhibitory than from Pa grown planktonically. The differences in Pa shown from these different sources are consistent with the extensive evolutionary Pa changes that have been described in association with chronic residence in CF airways, and may reflect adaptive changes to life in a polymicrobial environment.

Brownian bridge and other path-dependent Gaussian processes vectorial simulation

The iterative simulation of the Brownian bridge is well known. In this article, we present a vectorial simulation alternative based on Gaussian processes for machine learning regression that is suitable for interpreted programming languages implementations. We extend the vectorial simulation of path-dependent trajectories to other Gaussian processes, namely, sequences of Brownian bridges, geometric Brownian motion, fractional Brownian motion, and Ornstein-Ulenbeck mean reversion process.

Lethality of triatomines (Hemiptera: Reduviiae), vectors of Chagas' disease, feeding on blood baits containing synthetic insecticides, under laboratory conditions

A laboratory study was conducted to test the toxicity of synthetic insecticides added to defibrinated sheep blood kept at room temperature and offered as food to the following triatomine species: Triatoma infestans, Panstrongylus megistus, Triatoma vitticeps, Triatoma pseudomaculata, Triatoma brasiliensis and Rhodnius prolixus. The insecticides used, at a concentration of 1g/l, were: HCH, DDT, Malathion and Trichlorfon, and the lethalithy observed at the end of a 7-day period varied according to the active principle of each. HCH was the most effective by the oral route, killing 100% of the insects, except P. megistus (95.7%) and T. pseudomaculata (94.1%). Trichlorfon killed the insects at rates ranging from 71.8% (T. vitticeps) to 98% (R. prolixus). Malathion was slightly less efficient, killing the insects at rates from 56.8% (T. vitticeps) to 97% (T.brasiliensis). DDT was the least effective, with a killing rate of 10% (T. vitticeps) to 75% (T.brasiliensis). Since the tests were performed at room temperature, we suggest that baits of this type should be tried for the control of triatomines in the field.

Mathematical model for HIV dynamics in HIV-specific helper cells

In this paper we study a delay mathematical model for the dynamics of HIV in HIV-specific CD4 + T helper cells. We modify the model presented by Roy and Wodarz in 2012, where the HIV dynamics is studied, considering a single CD4 + T cell population. Non-specific helper cells are included as alternative target cell population, to account for macrophages and dendritic cells. In this paper, we include two types of delay: (1) a latent period between the time target cells are contacted by the virus particles and the time the virions enter the cells and; (2) virus production period for new virions to be produced within and released from the infected cells. We compute the reproduction number of the model, R0, and the local stability of the disease free equilibrium and of the endemic equilibrium. We find that for values of R0<1, the model approaches asymptotically the disease free equilibrium. For values of R0>1, the model approximates asymptotically the endemic equilibrium. We observe numerically the phenomenon of backward bifurcation for values of R0⪅1. This statement will be proved in future work. We also vary the values of the latent period and the production period of infected cells and free virus. We conclude that increasing these values translates in a decrease of the reproduction number. Thus, a good strategy to control the HIV virus should focus on drugs to prolong the latent period and/or slow down the virus production. These results suggest that the model is mathematically and epidemiologically well-posed.

Hemolymphatic components in vectors of Trypanosoma cruzi: study in several species of the subfamily Triatominae (Hemiptera: Reduviidae)

The members of the subfamily Triatominae (Hemiptera : Reduviidae) comprise a great number of species of medical importance in the transmission of the T. cruzi (American trypanosomiasis). The aim of this study was to contribute to the knowledge about the chemical composition in proteins, lipids, lipoproteins, and carbohydrates of vectors of Chagas' disease corresponding to twelve members of the subfamily Triatominae. This study was carried out in ninphs of the fifth instar and adult males of the species: T. delpontei, T. dimidiata, T. guasayana, T. infestans, T. mazzotti, T. pallidipennis, T. patagonica, T. platensis, T. rubrovaria, T. sordida of the Triatoma genus, and D. maximus and P. megistus of the Dipatalogaster and Panstrongylus genera respectively. The results show on one hand, qualitative differences in the protein composition, and on the other hand, similarity in the lipoprotein profiles. Lipids, proteins, and carbohydrates did not show significant differences between species or/and stages.

Estrogen receptor dependent genetic and epigenetic factors of tamoxifen resistance

Resumo: A decisão da terapêutica hormonal no tratamento do cancro da mama baseiase na determinação do receptor de estrogénio alfa por imunohistoquímica (IHC). Contudo, a presença deste receptor não prediz a resposta em todas as situações, em parte devido a limitações do método IHC. Investigámos se a expressão dos genes ESR1 e ESR2, bem como a metilação dos respectivos promotores, pode estar relacionada com a evolução desfavorável de uma proporção de doentes tratados com tamoxifeno assim como com a perda dos receptores de estrogénio alfa (ERα) e beta (ERß). Amostras de 211 doentes com cancro da mama diagnosticado entre 1988 e 2004, fixadas em formalina e preservadas em parafina, foram utilizadas para a determinação por IHC da presença dos receptores ERα e ERß. O mRNA total do gene ESR1 e os níveis específicos do transcrito derivado do promotor C (ESR1_C), bem como dos transcritos ESR2_ß1, ESR2_ß2/cx, and ESR2_ß5 foram avaliados por Real-time PCR. Os promotores A e C do gene ESR1 e os promotores 0K e 0N do gene ESR2 foram investigados por análise de metilação dos dinucleotidos CpG usando bisulfite-PCR para análise com enzimas de restrição, ou para methylation specific PCR. Atendendo aos resultados promissores relacionados com a metilação do promotor do gene ESR1, complementamos o estudo com um método quantitativo por matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) suportado pelo software Epityper para a medição da metilação nos promotores A e C. Fez-se a avaliação da estabilidade do mRNA nas linhas celulares de cancro da mama MCF-7 e MDA-MB-231 tratadas com actinomicina D. Baixos níveis do transcrito ESR1_C associaram-se a uma melhor sobrevivência global (p = 0.017). Níveis elevados do transcrito ESR1_C associaram-se a uma resposta inferior ao tamoxifeno (HR = 2.48; CI 95% 1.24-4.99), um efeito mais pronunciado em doentes com tumores de fenótipo ERα/PgR duplamente positivo (HR = 3.41; CI 95% 1.45-8.04). A isoforma ESR1_C mostrou ter uma semi-vida prolongada, bem como uma estrutura secundária da região 5’UTR muito mais relaxada em comparação com a isoforma ESR1_A. A análise por Western-blot mostrou que ao nível da 21 proteína, a selectividade de promotores é indistinguivel. Não se detectou qualquer correlação entre os níveis das isoformas do gene ESR2 ou entre a metilação dos promotores do gene ESR2, e a detecção da proteína ERß. A metilação do promotor C do gene ESR1, e não do promotor A, foi responsável pela perda do receptor ERα. Estes resultados sugerem que os níveis do transcrito ESR1_C sejam usados como um novo potencial marcador para o prognóstico e predição de resposta ao tratamento com tamoxifeno em doentes com cancro da mama. Abstract: The decision of endocrine breast cancer treatment relies on ERα IHC-based assessment. However, ER positivity does not predict response in all cases in part due to IHC methodological limitations. We investigated whether ESR1 and ESR2 gene expression and respective promoter methylation may be related to non-favorable outcome of a proportion of tamoxifen treated patients as well as to ERα and ERß loss. Formalin-fixed paraffin-embedded breast cancer samples from 211 patients diagnosed between 1988 and 2004 were submitted to IHC-based ERα and ERß protein determination. ESR1 whole mRNA and promoter C specific transcript levels, as well as ESR2_ß1, ESR2_ß2/cx, and ESR2_ß5 transcripts were assessed by real-time PCR. ESR1 promoters A and C, and ESR2 promoters 0N and 0K were investigated by CpG methylation analysis using bisulfite-PCR for restriction analysis, or methylation specific PCR. Due to the promising results related to ESR1 promoter methylation, we have used a quantification method by matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDITOF MS) together with Epityper software to measure methylation at promoters A and C. mRNA stability was assessed in actinomycin D treated MCF-7 and MDA-MB-231 cells. ERα protein was quantified using transiently transfected breast cancer cells. Low ESR1_C transcript levels were associated with better overall survival (p = 0.017). High levels of ESR1_C transcript were associated with non-favorable response in tamoxifen treated patients (HR = 2.48; CI 95% 1.24-4.99), an effect that was more pronounced in patients with ERα/PgR double-positive tumors (HR = 3.41; CI 95% 1.45-8.04). The ESR1_C isoform had a prolonged mRNA half-life and a more relaxed 5’UTR structure compared to ESR1_A isoform. Western-blot analysis showed that at protein level, the promoter selectivity is undistinguishable. There was no correlation between levels of ESR2 isoforms or ESR2 promoter methylation and ERß protein staining. ESR1 promoter C CpG methylation and not promoter A was responsible for ERα loss. We propose ESR1_C levels as a putative novel marker for breast cancer prognosis and prediction of tamoxifen response.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.