Seasonal fouling by diatoms on artificial substrata at different depths near Piran (Gulf of Trieste, Northern Adriatic)

Seasonal fouling by diatoms was studied in the heavily polluted and eutrofied area near Piran in the Gulf of Trieste. Concrete plates (50 x 50 cm) were placed at l m, 3 m and 7 m depths, with the fouling observed monthly for one year, from March to October. Two plates were used at each level: one was scratched clean monthly to get an insight into the seasonality of fouling, while from the other only representative samples were taken in order to follow the fouling succession. In the eulittoral two quadrats of the same dimension were scratched clean on a vertical concrete wall. Diatoms proved to be the main fouling component sublittorally, while in the eulittoral green algae determined the physiognomy of the experi mental surfaces during spring. The present contribution deals only with the diatoms. Peaks of diatom colonization were found in April and August in the eulittoral, and sublittorally in July. Regarding the depth distribution, maxima in the number of recorded species were found at 3 m in spring, and at 7 m in autumn. The fouling populations were heterogenous, including epilithic, epipsammic and epipelic species with different affinities (marine, brackish and even freshwater). Colonial forms belonging to the genera Berkeleya, Navicula and Licmophora were outstanding and covered most of the experimental surfaces. Achnanthes species were among the primary colonizers, while Nitzschia species joined the fouling communities in autumn, along with several epipelic species. Seasonal recolonization on the monthly denuded plates was usual for species found sublittorally, either the whole year around, or only in autumn. Species found during spring did not recolonize monthly, and the same was true of the eulittoral ones

Colonization by benthic algae on submarine in the southeastern area of the Gulf of Trieste

Scopo dello studio è stato quello di analizzare i processi di colonizzazione macroalgale su differenti tipi di substrati artificiali, in due aree del Golfo di Trieste (area nord-occidentale, area sud-orientale). Le due aree sono ecologicamente differenti, in termini di idrodinamismo, batimetria, esposizione a moto ondoso, sedimentazione, torpidità e struttura geologica del fondale. Tali fattori influenzano in maniera differente il processo di colonizzazione algale. Le fasi delle colonizzazioni macroalgali sono state seguite mediante immersioni subacquee dopo 2,6,12 e 24 mesi e i campioni sono stati prelevati tramite grattaggi del substrato. Per l’analisi dei campionamenti è stata utilizzata una scala arbitraria, già utilizzata in precedenti studi (Munda, 1978, 1979, 1991a, 2005). Dall’analisi dei campioni sono state osservate variazioni nel popolamento di alcune specie appartenenti ai gruppi Cystoseira e Sargassum come anche nelle associazioni di Fucales, quasi scomparse dal Golfo. In altri casi invece variazioni spazio-temporali (stagionalità) nella colonizzazione, sono state relazionate con la profondità del fondale, la tipologia dei substrati utilizzati e le diverse strategie di colonizzazione in particolare per quanto riguarda diatomee e macroalghe

Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p