Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression

Hard and soft exudates are the main signs of diabetic macular edema (DME). The segmentation of both kinds of exudates generates valuable information not only for the diagnosis of DME, but also for treatment, which helps to avoid vision loss and blindness. In this paper, we propose a new algorithm for the automatic segmentation of exudates in ocular fundus images. The proposed algorithm is based on ensembles of aperture filters that detect exudate candidates and remove major blood vessels from the processed images. Then, logistic regression is used to classify each candidate as either exudate or non-exudate based on a vector of 31 features that characterize each potensial lesion. Finally, we tested the performance of the proposed algorithm using the images in the public HEI-MED database.Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación; EcuadorFil: Brun, Marcel. Universidad Nacional de Mar del Plata; ArgentinaFil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; Argentin

Linguistic Interpretation of Mathematical Morphology

Mathematical Morphology is a theory based on geometry, algebra, topology and set theory, with strong application to digital image processing. This theory is characterized by two basic operators: dilation and erosion. In this work we redefine these operators based on compensatory fuzzy logic using a linguistic definition, compatible with previous definitions of Fuzzy Mathematical Morphology. A comparison to previous definitions is presented, assessing robustness against noise.Fil: Bouchet, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata; ArgentinaFil: Meschino, Gustavo. Universidad Nacional de Mar del Plata; ArgentinaFil: Brun, Marcel. Universidad Nacional de Mar del Plata; ArgentinaFil: Espin Andrade, Rafael. Instituto Superior Politécnico José Antonio Echeverría Cujae; CubaFil: Ballarin, Virginia. Universidad Nacional de Mar del Plata; Argentin

On the Number of Close-to-Optimal Feature Sets

The issue of wide feature-set variability has recently been raised in the context of expression-based classification using microarray data. This paper addresses this concern by demonstrating the natural manner in which many feature sets of a certain size chosen from a large collection of potential features can be so close to being optimal that they are statistically indistinguishable. Feature-set optimality is inherently related to sample size because it only arises on account of the tendency for diminished classifier accuracy as the number of features grows too large for satisfactory design from the sample data. The paper considers optimal feature sets in the framework of a model in which the features are grouped in such a way that intra-group correlation is substantial whereas inter-group correlation is minimal, the intent being to model the situation in which there are groups of highly correlated co-regulated genes and there is little correlation between the co-regulated groups. This is accomplished by using a block model for the covariance matrix that reflects these conditions. Focusing on linear discriminant analysis, we demonstrate how these assumptions can lead to very large numbers of close-to-optimal feature sets

HMMER cut-off threshold tool (HMMERCTTER): Supervised classification of superfamily protein sequences with a reliable cut-off threshold

Background: Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear subfamily hallmarks that would allow pattern-based function assignation by which this task is mostly achieved based on the similarity principle. This is hampered by the lack of a score cut-off that is both sensitive and specific. Results: HMMER Cut-off Threshold Tool (HMMERCTTER) adds a reliable cut-off threshold to the popular HMMER. Using a high quality superfamily phylogeny, it clusters a set of training sequences such that the cluster-specific HMMER profiles show cluster or subfamily member detection with 100% precision and recall (P&R), thereby generating a specific threshold as inclusion cut-off. Profiles and thresholds are then used as classifiers to screen a target dataset. Iterative inclusion of novel sequences to groups and the corresponding HMMER profiles results in high sensitivity while specificity is maintained by imposing 100% P&R self detection. In three presented case studies of protein superfamilies, classification of large datasets with 100% precision was achieved with over 95% recall. Limits and caveats are presented and explained. Conclusions: HMMERCTTER is a promising protein superfamily sequence classifier provided high quality training datasets are used. It provides a decision support system that aids in the difficult task of sequence function assignation in the twilight zone of sequence similarity. All relevant data and source codes are available from the Github repository at the following.Fil: Pagnuco, Inti Anabela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Revuelta, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; ArgentinaFil: Bondino, Hernán Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; ArgentinaFil: Brun, Marcel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Ten Have, Arjen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Biológicas. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; Argentin

Which Is Better: Holdout or Full-Sample Classifier Design?

Is it better to design a classifier and estimate its error on the full sample or to design a classifier on a training subset and estimate its error on the holdout test subset? Full-sample design provides the better classifier; nevertheless, one might choose holdout with the hope of better error estimation. A conservative criterion to decide the best course is to aim at a classifier whose error is less than a given bound. Then the choice between full-sample and holdout designs depends on which possesses the smaller expected bound. Using this criterion, we examine the choice between holdout and several full-sample error estimators using covariance models and a patient-data model. Full-sample design consistently outperforms holdout design. The relation between the two designs is revealed via a decomposition of the expected bound into the sum of the expected true error and the expected conditional standard deviation of the true error

Comparison of Gene Regulatory Networks via Steady-State Trajectories

The modeling of genetic regulatory networks is becoming increasingly widespread in the study of biological systems. In the abstract, one would prefer quantitatively comprehensive models, such as a differential-equation model, to coarse models; however, in practice, detailed models require more accurate measurements for inference and more computational power to analyze than coarse-scale models. It is crucial to address the issue of model complexity in the framework of a basic scientific paradigm: the model should be of minimal complexity to provide the necessary predictive power. Addressing this issue requires a metric by which to compare networks. This paper proposes the use of a classical measure of difference between amplitude distributions for periodic signals to compare two networks according to the differences of their trajectories in the steady state. The metric is applicable to networks with both continuous and discrete values for both time and state, and it possesses the critical property that it allows the comparison of networks of different natures. We demonstrate application of the metric by comparing a continuous-valued reference network against simplified versions obtained via quantization

Inhibitory Activity of Marine Sponge-Derived Natural Products against Parasitic Protozoa

In this study, thirteen sponge-derived terpenoids, including five linear furanoterpenes: furospinulosin-1 (1), furospinulosin-2 (2), furospongin-1 (3), furospongin-4 (4), and demethylfurospongin-4 (5); four linear meroterpenes: 2-(hexaprenylmethyl)-2-methylchromenol (6), 4-hydroxy-3-octaprenylbenzoic acid (7), 4-hydroxy-3-tetraprenyl-phenylacetic acid (8), and heptaprenyl-p-quinol (9); a linear triterpene, squalene (10); two spongian-type diterpenes dorisenone D (11) and 11β-acetoxyspongi-12-en-16-one (12); a scalarane-type sesterterpene; 12-epi-deoxoscalarin (13), as well as an indole alkaloid, tryptophol (14) were screened for their in vitro activity against four parasitic protozoa; Trypanosoma brucei rhodesiense, Trypanosoma cruzi, Leishmania donovani and Plasmodium falciparum. Cytotoxic potential of the compounds on mammalian cells was also assessed. All compounds were active against T. brucei rhodesiense, with compound 8 being the most potent (IC50 0.60 μg/mL), whereas 9 and 12 were the most active compounds against T. cruzi, with IC50 values around 4 μg/mL. Compound 12 showed the strongest leishmanicidal activity (IC50 0.75 μg/mL), which was comparable to that of miltefosine (IC50 0.20 μg/mL). The best antiplasmodial effect was exerted by compound 11 (IC50 0.43 μg/mL), followed by compounds 7, 10, and 12 with IC50 values around 1 μg/mL. Compounds 9, 11 and 12 exhibited, besides their antiprotozoal activity, also some cytotoxicity, whereas all other compounds had low or no cytotoxicity towards the mammalian cell line. This is the first report of antiprotozoal activity of marine metabolites 1–14, and points out the potential of marine sponges in discovery of new antiprotozoal lead compounds

Alkamides from Anacyclus pyrethrum L. and their in vitro antiprotozoal activity

In our ongoing study to evaluate the antiprotozoal activity of alkamides from Asteraceae, a dichloromethane extract from the roots of Anacycluspyrethrum L. showed a moderate in vitro activity against the NF54 strain of Plasmodium falciparum and against Leishmaniadonovani (amastigotes, MHOM/ET/67/L82 strain). Seven pure alkamides and a mixture of two further alkamides were isolated by column chromatography followed by preparative high performance liquid chromatography. The alkamides were identified by mass- and NMR-spectroscopic methods as tetradeca-2E,4E-dien-8,10-diynoic acid isobutylamide (anacycline, 1), deca-2E,4E-dienoic acid isobutylamide (pellitorine, 2), deca-2E,4E,9-trienoic acid isobutylamide (3), deca-2E,4E-dienoic acid 2-phenylethylamide (4), undeca-2E,4E-dien-8,10-diynoic acid isopentylamide (5), tetradeca-2E,4E,12Z-trien-8,10-diynoic acid isobutylamide (6), and dodeca-2E,4E-dien acid 4-hydroxy-2-phenylethylamide (7). Two compounds-undeca-2E,4E-dien-8,10-diynoic acid 2-phenylethylamide (8) and deca-2E,4E-dienoic acid 4-hydroxy-2-phenylethylamide (9)-were isolated as an inseparable mixture (1:4). Compounds 3, 4, and 5 were isolated from Anacycluspyrethrum L. for the first time. While compounds 4 and 5 were previously known from the genus Achillea, compound 3 is a new natural product, to the best of our knowledge. All isolated alkamides were tested in vitro for antiprotozoal activity against Plasmodium falciparum, Trypanosomabruceirhodesiense, Trypanosomacruzi, and Leishmaniadonovani and for cytotoxicity against L6 rat skeletal myoblasts

Sesquiterpene lactones from Vernonia cinerascens sch. bip. and their in vitro antitrypanosomal activity

In the endeavor to obtain new antitrypanosomal agents, particularly sesquiterpene lactones, from Kenyan plants of the family Asteraceae, Vernonia cinerascens Sch. Bip. was investigated. Bioactivity-guided fractionation and isolation in conjunction with LC/MS-based dereplication has led to the identification of vernodalol (1) and isolation of vernodalin (2), 11β,13-dihydrovernodalin (3), 11β,13-dihydrovernolide (4), vernolide (5), 11β,13-dihydrohydroxyvernolide (6), hydroxyvernolide (7), and a new germacrolide type sesquiterpene lactone vernocinerascolide (8) from the dichloromethane extract of V. cinerascens leaves. Compounds 3-8 were characterized by extensive analysis of their 1D and 2D NMR spectroscopic and HR/MS spectrometric data. All the compounds were evaluated for their in vitro biological activity against bloodstream forms of Trypanosoma brucei rhodesiense and for cytotoxicity against the mammalian cell line L6. Vernodalin (2) was the most active compound with an IC50 value of 0.16 µM and a selectivity index of 35. Its closely related congener 11β,13-dihydrovernodalin (3) registered an IC50 value of 1.1 µM and a selectivity index of 4.2