Shannon information and power law analysis of the chromosome code

This paper studies the information content of the chromosomes of twenty-three species. Several statistics considering different number of bases for alphabet character encoding are derived. Based on the resulting histograms, word delimiters and character relative frequencies are identified. The knowledge of this data allows moving along each chromosome while evaluating the flow of characters and words. The resulting flux of information is captured by means of Shannon entropy. The results are explored in the perspective of power law relationships allowing a quantitative evaluation of the DNA of the species.

Dimensioning and worst-case analysis of cluster-tree sensor networks

Modeling the fundamental performance limits of Wireless Sensor Networks (WSNs) is of paramount importance to understand their behavior under the worst-case conditions and to make the appropriate design choices. This is particular relevant for time-sensitive WSN applications, where the timing behavior of the network protocols (message transmission must respect deadlines) impacts on the correct operation of these applications. In that direction this paper contributes with a methodology based on Network Calculus, which enables quick and efficient worst-case dimensioning of static or even dynamically changing cluster-tree WSNs where the data sink can either be static or mobile. We propose closed-form recurrent expressions for computing the worst-case end-to-end delays, buffering and bandwidth requirements across any source-destination path in a cluster-tree WSN. We show how to apply our methodology to the case of IEEE 802.15.4/ZigBee cluster-tree WSNs. Finally, we demonstrate the validity and analyze the accuracy of our methodology through a comprehensive experimental study using commercially available technology, namely TelosB motes running TinyOS.

Film thickness in a ball-on-disc contact lubricated with greases, bleed oils and base oils

Three different lubricating greases and their bleed and base oils were compared in terms of film thickness in a ball-on-disc test rig through optical interferometry. The theoretical values calculated according to Hamrock's equation are in close agreement with the base oil film thickness measurements, which validates the selected experimental methodology. The grease and bleed oil film thickness under fully flooded lubrication conditions presented quite similar behaviour and levels. Therefore, the grease film thickness under full film conditions might be predicted using their bleed oil properties, namely the viscosity and pressure-viscosity coefficient. The base and bleed oil lubricant parameter LP are proportional to the measured film thickness. A relationship between grease and the corresponding bleed oil film thickness was evidenced.

In-plane displacement and strain image analysis

Measurements in civil engineering load tests usually require considerable time and complex procedures. Therefore, measurements are usually constrained by the number of sensors resulting in a restricted monitored area. Image processing analysis is an alternative way that enables the measurement of the complete area of interest with a simple and effective setup. In this article photo sequences taken during load displacement tests were captured by a digital camera and processed with image correlation algorithms. Three different image processing algorithms were used with real images taken from tests using specimens of PVC and Plexiglas. The data obtained from the image processing algorithms were also compared with the data from physical sensors. A complete displacement and strain map were obtained. Results show that the accuracy of the measurements obtained by photogrammetry is equivalent to that from the physical sensors but with much less equipment and fewer setup requirements. © 2015Computer-Aided Civil and Infrastructure Engineering.

Biomass production of four Cynara cardunculus clones and lignin composition analysis

Four Cynara cardunculus clones, two from Portugal and two from Spain were studied for biomass production and their lignin was characterized. The clones differed in biomass partitioning: Spanish clones produced more capitula (54.5% vs. 43.9%), and Portuguese clones more stalks (37.2% vs. 25.6%). The heating values (HHV0) of the stalks were similar, ranging from 17.1 to 18.4 MJ/kg. Lignin was studied by analytical pyrolysis (Py-GC/MS(FID)), separately in depithed stalks (stalksDP) and pith. StalksDP had in average higher relative proportions of lignin derived compounds than pith (23.9% vs. 21.8%) with slightly different lignin monomeric composition: pith samples were richer in syringyl units as compared to stalksDP (64% vs. 53%), with S/G ratios of 2.1 and 1.3, respectively. The H:G:S composition was 7:40:53 in stalksDP and 7:29:64 in pith. The lignin content ranged from 18.8% to 25.5%, enabling a differentiation between clones and provenances. © 2015 Elsevier Ltd. All rights reserved.

Chronic disease prevention and health promotion. Public policies and social network analysis of the hospitals services and oyhers health and social organizations to support joint action for children with diabetes

Comunicação apresentada na 18th Conference International of Health Promotion Hospitals & Health Services "Tackling causes and consequences of inequalities in health: contributions of health services and the HPH network", em Manchester de 14-16 de april de 2010

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. 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. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Anti-HCV related to HCV PCR and risk factors analysis in a blood donor population of Central Brazil

Data concerning HCV infection in Central Brazil are rare. Upon testing 2,350 voluntary blood donors from this region, we found anti-HCV prevalence rates of 2.2% by a second generation ELISA and 1.4% after confirmation by a line immunoassay. Antibodies against core, NS4, and NS5 antigens of HCV were detected in 81.8%, 72.7%, and 57.5%, respectively, of the positive samples in the line immunoassay. HCV viremia was present in 76.6% of the anti-HCV-positive blood donors. A relation was observed between PCR positivity and serum reactivity in recognizing different HCV antigens in the line immunoassay. The majority of the positive donors had history of previous parenteral exposure. While the combination of ALT>50 IU/l and anti-HBc positivity do not appear to be good surrogate markers for HCV infection, the use of both ALT anti-HCV tests is indicated in the screening of Brazilian blood donors.

Cochineal, a precious source of red: cochineal dyes characterization by high performance liquid chromatography with diode array detection and principal component analysis

Presented at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles

Behavioral and synaptic circuit analysis in models of neuropsychiatric disorders: Dissecting the in vivo role of the postsynaptic density proteins nArgBP2 and Shank3 using genetic engineered mice

Dissertation presented to obtain the Ph.D degree in Biology

Comprehensive treatments for social cognitive deficits in schizophrenia: A critical review and effect-size analysis of controlled studies

Recent advances in psychosocial treatments for schizophrenia have targeted social cognitive deficits. A critical literature review and effect-size (ES) analysis was conducted to investigate the efficacy of comprehensive programs of social cognitive training in schizophrenia. Results revealed 16 controlled studies consisting of seven models of comprehensive treatment with only three of these treatment models investigated in more than one study. The effects of social cognitive training were reported in 11/15 studies that included facial affect recognition skills (ES=.84) and 10/13 studies that included theory-of-mind (ES=.70) as outcomes. Less than half (4/9) of studies that measured attributional style as an outcome reported effects of treatment, but effect sizes across studies were significant (ESs=.30-.52). The effect sizes for symptoms were modest, but, with the exception of positive symptoms, significant (ESs=.32-.40). The majority of trials were randomized (13/16), selected active control conditions (11/16) and included at least 30 participants (12/16). Concerns for this area of research include the absence of blinded outcome raters in more than 50% of trials and low rates of utilization of procedures for maintaining treatment fidelity. These findings provide preliminary support for the broader use of comprehensive social cognitive training procedures as a psychosocial intervention for schizophrenia.

MOLECULAR CHARACTERIZATION AND SEQUENCE PHYLOGENETIC ANALYSIS OF SURFACE ANTIGEN 3 (SAG3) GENE OF LOCAL INDIAN ISOLATES (CHENNAI AND IZATNAGAR) OF Toxoplasma gondii

Context and objective:The molecular characterization of local isolates of Toxoplasma gondii is considered significant so as to assess the homologous variations between the different loci of various strains of parasites.Design and setting:The present communication deals with the molecular cloning and sequence analysis of the 1158 bp entire open reading frame (ORF) of surface antigen 3 (SAG3) of two Indian T. gondii isolates (Chennai and Izatnagar) being maintained as cryostock at the IVRI.Method:The surface antigen 3 (SAG3) of two local Indian isolates were cloned and sequenced before being compared with the available published sequences.Results:The sequence comparison analysis revealed 99.9% homology with the standard published RH strain sequence of T. gondii. The strains were also compared with other established published sequences and found to be most related to the P-Br strain and CEP strain (both 99.3%), and least with PRU strain (98.4%). However, the two Indian isolates had 100% homology between them.Conclusion:Finally, it was concluded that the Indian isolates were closer to the RH strain than to the P-Br strain (Brazilian strain), the CEP strain and the PRU strains (USA), with respect to nucleotide homology. The two Indian isolates used in the present study are known to vary between themselves, as far as homologies related to other genes are concerned, but they were found to be 100% homologous as far as SAG3 locus is concerned. This could be attributed to the fact that this SAG3 might be a conserved locus and thereby, further detailed studies are thereby warranted to exploit the use of this particular molecule in diagnostics and immunoprophylactics. The findings are important from the point of view of molecular phylogeny.

HIV-2 Integrase Polymorphisms and Longitudinal Genotypic Analysis of HIV-2 Infected Patients Failing a Raltegravir-Containing Regimen

To characterize the HIV-2 integrase gene polymorphisms and the pathways to resistance of HIV-2 patients failing a raltegravir-containing regimen, we studied 63 integrase strand transfer inhibitors (INSTI)-naïve patients, and 10 heavily pretreated patients exhibiting virological failure while receiving a salvage raltegravir-containing regimen. All patients were infected by HIV-2 group A. 61.4% of the integrase residues were conserved, including the catalytic motif residues. No INSTI-major resistance mutations were detected in the virus population from naïve patients, but two amino acids that are secondary resistance mutations to INSTIs in HIV-1 were observed. The 10 raltegravir-experienced patients exhibited resistance mutations via three main genetic pathways: N155H, Q148R, and eventually E92Q - T97A. The 155 pathway was preferentially used (7/10 patients). Other mutations associated to raltegravir resistance in HIV-1 were also observed in our HIV-2 population (V151I and D232N), along with several novel mutations previously unreported. Data retrieved from this study should help build a more robust HIV-2-specific algorithm for the genotypic interpretation of raltegravir resistance, and contribute to improve the clinical monitoring of HIV-2-infected patients.

Ocular Pulse Amplitude and Doppler Waveform Analysis in Glaucoma Patients

PURPOSE: To determine the correlation between ocular blood flow velocities and ocular pulse amplitude (OPA) in glaucoma patients using colour Doppler imaging (CDI) waveform analysis. METHOD: A prospective, observer-masked, case-control study was performed. OPA and blood flow variables from central retinal artery and vein (CRA, CRV), nasal and temporal short posterior ciliary arteries (NPCA, TPCA) and ophthalmic artery (OA) were obtained through dynamic contour tonometry and CDI, respectively. Univariate and multiple regression analyses were performed to explore the correlations between OPA and retrobulbar CDI waveform and systemic cardiovascular parameters (blood pressure, blood pressure amplitude, mean ocular perfusion pressure and peripheral pulse). RESULTS: One hundred and ninety-two patients were included [healthy controls: 55; primary open-angle glaucoma (POAG): 74; normal-tension glaucoma (NTG): 63]. OPA was statistically different between groups (Healthy: 3.17 ± 1.2 mmHg; NTG: 2.58 ± 1.2 mmHg; POAG: 2.60 ± 1.1 mmHg; p < 0.01), but not between the glaucoma groups (p = 0.60). Multiple regression models to explain OPA variance were made for each cohort (healthy: p < 0.001, r = 0.605; NTG: p = 0.003, r = 0.372; POAG: p < 0.001, r = 0.412). OPA was independently associated with retrobulbar CDI parameters in the healthy subjects and POAG patients (healthy CRV resistance index: β = 3.37, CI: 0.16-6.59; healthy NPCA mean systolic/diastolic velocity ratio: β = 1.34, CI: 0.52-2.15; POAG TPCA mean systolic velocity: β = 0.14, CI 0.05-0.23). OPA in the NTG group was associated with diastolic blood pressure and pulse rate (β = -0.04, CI: -0.06 to -0.01; β = -0.04, CI: -0.06 to -0.001, respectively). CONCLUSIONS: Vascular-related models provide a better explanation to OPA variance in healthy individuals than in glaucoma patients. The variables that influence OPA seem to be different in healthy, POAG and NTG patients.

Open innovation and social network analysis

The present article is based on the report for the Doctoral Conference of the PhD programme in Technology Assessment, held at FCT-UNL Campus, Monte de Caparica, July 9th, 2012. The PhD thesis has the supervision of Prof. Cristina Sousa (ISCTE-IUL), and co-supervision of Prof. José Cardoso e Cunha (FCT-UNL).