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Phytoplankton sinking is an important property that can determine community composition, affecting nutrient and light absorption in the photic zone, and influencing biogeochemical cycling via material loss to the deep ocean. To date, the difficulty in exploring the sinking processes is partly due to methodological limitations in measuring phytoplankton sinking rate. However, in the last decade, works have illustrated various methods based on some non-invasive and low perturbing approaches (laser scanner, video-microscopy, fluorescence spectroscopy). In this study, we review the methods for sinking rate estimation and describe the Plankton Tracker, a novel integrated system to investigate in vivo the dynamic sinking behavior of phytoplankton. Plankton Tracker is composed of a long-distance objective and a video-microscopy facility coupled with a specifically developed image analysis system (Plankton Tracker Elements). Diatoms and dinoflagellates were individually-tracked. By processing recorded videos and images, sinking traits (e.g. trajectory length and complexity, sinking velocity, turning angle) were obtained simultaneously with morphological traits (e.g. linear dimensions). We also applied a Biased Correlated Random Walk model to sinking traits. Particularly, we found linear motion in the colony-forming Thalassiosira sp. and sinusoidal reorientation in the dinoflagellate Scrippsiella acuminata. We found complex suspension dynamic of the large Coscinodiscus sp. In this view, the Plankton Tracker approach is likely to provide more insights towards the exploration of dynamic sinking behavior in phytoplankton.

This work is a part of the MAGIC-5 (Medical Applications on a Grid Infrastructure Connection) experiment of the Italian INFN (Istituto Nazionale di Fisica Nucleare). A simple CAD (Computer-Assisted Detection) system for juxta-pleural lung nodules in CT images is presented, with the purpose of comparing different 2D concavity-patching techniques and assessing the respective efficiency in locating nodules. After a short introduction on the motivation, and a review of some CAD systems for lung nodules already published by the MAGIC-5 Collaboration, the paper describes the main lines of this particular approach, giving preliminary results and comments. In our procedure, candidate nodules are identified by patching lung border concavities in a hierarchical multiscale framework. Once located, they are fed to an artificial neural network for false positive reduction. The system has a modular structure that easily allows the insertion of arbitrary border-smoothing functions for concavity detection and nodule searching. In this paper the α-hull and morphological closing are compared, proving the higher sensitivity of the former, which also appears computationally less heavy. © 2010 Elsevier B.V. All rights reserved.

The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters. Once the features are computed for each ROI, they are used as inputs to a supervised neural network with momentum. The output neuron provides the probability that the ROI is pathological or not. Results are provided in terms of ROC and FROC curves: the area under the ROC curve was found to be Az=0.862plusmn0.007, and we get a 2.8 FP/Image at a sensitivity of 82%. This software is included in the CAD station actually working in the hospitals belonging to the MAGIC-5 Collaboration

A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by twelve morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with 6 FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).

A study of the problem of deterioration of a historical monument, the Crypt of the "Cattedrale di Otranto” (Lecce, South Italy), integrating different non destructive techniques (microclimatic and Ground Penetrating Radar) is proposed. In literature the degradation of stone is attributed to the presence of water and/or moisture in the porous material that occurs in water-damaged and humid buildings, due to poorly manufactured constructions and inadequate maintenance. The distribution of moisture within stone mainly depends on unsuitable environmental conditions and the presence of wet buried structures in the ground. After microclimatic surveys, through which we assessed unsuitable condition in the Crypt, Ground Penetrating radar (GPR) surveys were carried out. A careful analysis of the 0.4 m spaced GPR parallel profiles, acquired in the Crypt using a Sir2 GSSI radar system with a 500 MHz antenna, allow us to distinguish the wet and the moisture zone. GPR data was displayed in time slices form to obtain both the amplitude and the moisture variations map. Moreover GPR data was displayed in the form of frequency slices to highlight absorption losses probably related to higher moisture content. Therefore the GPR data was compared with the microclimatic data, and a good agreement was found, suggesting that an important channelling of moisture has its origin in the subsoil.

Conducting experiments in physics using modern measuring techniques, and particularly those utilizing computers, is often much more attractive to students than conducting experiments conventionally. However, the cost of professional kits in the Czech Republic is still very expensive for many schools. The basic equipment for one student workplace in the case of professional kits such as Vernier, Pasco or Coach costs around 800 euros. In this paper some physics experiments in which a computer, or a tablet with Microsoft Windows, is used as the measuring device, along with available physical devices such as a laser pointer, a solar cell or an electret microphone, are presented as suitable and alternative ways to carry out lab work. We show that it is possible to perform very simple school experiments (both as a central demonstration and as individual experimentation), in which high accuracy and clear final conclusions can be achieved at a very low cost. Further information is published on the specialized webpage www.sclpx.eu/ index.php?lang=en. The worksheets are in Czech, but the English version is in preparation.

The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (computed tomography) images.

In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) die training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features extracted from co-occurrence matrix containing spatial statistics information on ROI pixel gray tones. A dissimilarity representation of diese features is made before me classification. A feed-forward neural network is employed to distinguish pathological records, from non-pathological ones by the new features. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correcdy classified) will be presented.

SiO2 films have been deposited by ion-beam sputtering techniques either without assistance or with Ar-ion assistance and the role of the substrate temperature during the deposition process on the film damage threshold by laser light at the wavelength of 308 nm (XeCl) has been investigated. The photoacoustic probe beam deflection technique has been used to determine damage thresholds and it is shown that this technique provides valuable information on the mechanisms leading to laser damage. It has been found that the samples deposited by the Ar-ion beam sputtering technique are characterized by higher damage thresholds at lower temperatures of the substrate than the SiO2 films deposited without ion assistance.

A XeCl laser delivering circularly polarised laser pulses with quite low beam-quality factor ( M 2 ≅4) is focused in H 2 to generate rotational and vibrational lines by stimulated Raman scattering processes. The efficiency for generation of the first rotational line S 01 at 313.7 nm ( v =0, J =1→ v =0, J =3) is improved by tightly focusing the pump beam in the Raman cell as a consequence of vibrational Raman gain suppression.

Stimulated rotational and vibrational Raman scattering in H/sub 2/ was investigated with a circularly and a linearly polarized XeCl laser beam and the influence of the focusing geometry on the threshold for rotational and vibrational scattering, respectively, was studied. It is shown that with a circularly polarized pump beam a high-angle focusing geometry allows to get only rotational Raman scattering, whereas a low-angle focusing geometry provides only vibrational Raman scattering for gain suppression effects.

A plane-parallel cavity with a Gaussian reflectivity mirror has been applied to a XeCl laser medium having a spatially variable gain distribution. The evolution of the beam parameters has been experimentally analyzed as a function of the number of round trips. It is shown that in this case of intensity profiles affected by the spatial and temporal properties of the active medium gain, a theory formerly developed for the beam-parameter evolution of Gaussian Schell-model beams provides a satisfactory description of experimental measurements for the first few round trips.

Objectives We detail a procedure for generating a set of templates for the hippocampal region in Magnetic Resonance images, representative of the clinical conditions of the population under investigation. Methods The first step is robust standardization of the intensity scale of brain MR images, belonging to patients with different degrees of neuropathology (Alzheimer’s Disease). So similar tissues have similar intensities, even across images coming from different sources. After the automatic extraction of the hippocampal region from a large dataset of images, we address template generation, choosing by clusterization methods a small number of the extracted regions. Results We assess that template generation is largely independent on the clusterization method and on the number and the clinical condition of the patients. The templates are chosen as the most representative images in a population. The estimation of the ‘minimum’ number of templates for the hippocampal region can be proposed, using a metric based on the geometrical position of the extracted regions. Conclusions This study describes a simple and easily reproducible procedure to generate templates for the hippocampal region. It can be generalized and applied to other brain regions, which may be relevant for neuroimaging studies.

Tumor cells in cerebral glioma invade the surrounding tissues preferentially along white-matter tracts, spreading beyond the abnormal area seen on conventional MR images. Diffusion Tensor Imaging can reveal large peritumoral abnormalities in gliomas, which are not apparent on MRI. Our aim was to characterize pathological vs. healthy tissue in DTI datasets by 3D statistical Texture Analysis, developing an automatic segmentation technique (CAD, Computer Assisted Detection) for cerebral glioma based on a supervised classifier (an artificial neural network). A Matlab GUI ( Graphical User Interface ) was created to help the physician in the assisted diagnosis process and to optimize interactivity with the segmentation system, especially for patient follow-up during chemotherapy, and for preoperative assessment of tumor extension. Preliminary tissue classification results were obtained for the p map (the calculated area under the ROC curve, AUC, was 0.96) and the FA map (AUC=0.98). Test images were automatically segmented by tissue classification; manual and automatic segmentations were compared, showing good concordance.

Our group has started a project aiming at the development of analysis algorithms devoted to images produced by two-dimensional electrophoresis (2-DE) for general applications in the field of proteomics. General analysis problems have been addressed, with the ultimate goal of the maximization of the "automation level" of the analysis procedure, without reducing the accuracy now reachable only with manual intervention. For this, the group is analyzing 2-DE images that are already stored or that will be produced by means of the instrumentation and the know-how of the biologists participating to the project. It's obvious that the "key" for the project success is primarily the availability of many images produced by different groups, but especially their "comparability", and their significant "clinical contextualization". For these reasons, the group has started the implementation of federation of heterogeneous bio banks of images founded on open Registry/Repository for P2P architecture (ebXML RR)[1], on images stored with standard metadata for their comparability (EFMI MIP) [2], and on standard electronic documents for their clinical contextualization (HL7 CDA) [3]. This paper summarizes the concepts of the initiative and describes possibility of handling data and analysing them under different perspectives.

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images with 1.25 mm slice thickness is presented. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The results obtained on the collected database of lung CT scans are discussed., 3 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference, Oct. 29 - Nov. 4, 2006, San Diego, California

A mobile instrument has been developed for the detection and mapping of detachments in frescos by using Compton back scattered photons. The instrument is mainly composed of a high energy X-ray tube, an X-ray detection system and a translation table. The instrument was first applied to samples simulating various detachment situations, and then transferred to the Vatican Museum to detect detachments and inhomogeneities in the stanza di Eliodoro, one of the "Raphael's stanze". © 2005 Elsevier B.V. All rights reserved.

A new investigation of the temperature dependence of the far-infrared spectra of acetanilide and some isotopomers is presented. Four absorption bands are considered at 31, 42, 64, and 80 cm −1 , and no significant change of their integrated intensity is observed when reducing the temperature. The temperature induced frequency shift values and other properties of these bands are consistent with an assignment as anharmonic lattice phonons. These results rule out the assignment of the 64, 80, and 106 cm −1 bands as normal modes of the polaronic excitation, as previously suggested.

The MAGIC-5 Project aims at developing Computer Aided Detection (CAD) software for Medical Applications on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images has been demonstrated to improve the radiologist performance. In addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of GRID Services for the management of distributed computing resources and data. GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated to the diagnosis in screening programs. The MAGIC-5 project develops algorithms for the analysis of mammographies for breast cancer detection, Computed-Tomography (CT) images for lung cancer detection and Positron Emission Tomography (PET) imaging for the early diagnosis of the Alzheimer Disease (AD). We plan to configure a Virtual Organization (VO), so that authorized users can share data and resources and implement the following use cases: screening, tele-training and tele-diagnosis for mammograms and lung CT scans; statistical diagnosis by comparison of candidates to a distributed data-set of negative PET scans for the diagnosis of the Alzheimer Disease. A smallscale prototype of the required GRID functionality was already implemented for the analysis of digitized mammograms.

The effects of climate, pollution and human negligence cause severe and sometimes irreversible damage to buildings and monuments of cultural interest. It is well known that the presence of water and/or moisture content in a porous material is the initial cause of deterioration. In a previous paper, the authors reported an integrated study on a building of cultural importance, namely the crypt of the Cattedrale di Otranto in Apulia, Italy, based on non-destructive integrated biological and physical surveys. The method described was able to identify the ‘internal’ factors responsible for deterioration. It was discovered that the distribution of moisture in the stone depended mainly on adverse environmental conditions, and the presence of wet buried structures in the ground. The first aim of the present study was to identify subsurface water-content in this same crypt using a ground-penetrating radar (GPR) technique, and to compare these results with those of the previous microclimatic survey. In particular, the existence of underground discontinuities was verified; we located them and analysed their influence. Moreover, by means of velocity analysis, we obtained a quantitative estimate of the volumetric water-content under the pavement of the crypt. This finding completes the results of the previous research, as it indicates the causes of the deterioration in the crypt and provides significant information, on the basis of which, effective decisions can be made for safeguarding the historic building.

We present a new percolative approach which simulates degradation towards failure of metallic or insulating thin films. The thin film is modelled as a two-dimensional random resistor network in thermal contact with a substrate. Its degradation is characterized by a breaking probability of the single resistor related to the substrate temperature and to Joule heating effects. The dependence of the single resistor on the local temperature is taken into account as well as its thermal interaction with nearest neighbours. The failure of metallic strips and ultra-thin dielectrics are then described as a conductor-insulator (CI) and an insulator-conductor (IC) transition, respectively. A recovery of the damage competing with the degradation is introduced which accounts for healing processes and can eventually lead to a steady-state condition. This approach can model the resistance degradation of metal interconnects by electromigration phenomena and the pre-breakdown of ultrathin dielectric where stress induced leakage currents (SILC) are used to monitor the degradation. The main features of experiments are reproduced together with their statistical properties. Our approach thus provides a unified framework for studying degradation and failure in both metallic and dielectric thin films.

Le applicazioni della Fisica e dell’Informatica alla Biomedicina includono i sistemi di individuazione di patologie (CAD, Computer-Assisted Detection) basati sul trattamento di dati provenienti da esami diagnostici (in particolare ma non limitandosi alle immagini diagnostiche quali TC, RM, etc.), gli strumenti di ausilio alla chirurgia (realtà virtuale, telechirurgia), l’analisi e l’interpretazione di segnali di interesse biomedicale (per esempio segnali da elettroencefalogramma, EEG, o da elettrocardiogramma, ECG). Questo lavoro presenta una rassegna di applicazioni, in cui gli autori sono impegnati, dandone alcuni dettagli implementativi e discutendone brevemente i risultati. Le applicazioni si differenziano per il tipo di dati analizzati (serie temporali provenienti da misure EEG, oppure dati bi- o tridimensionali contenuti in immagini diagnostiche), per il distretto corporeo di intervento, le finalità, la patologia.

Sono descritti i principi fisici della risonanza magnetica con particolare riferimento alla risonanza magnetica per immagini. Il fenomeno fisico è descritto sia da un punto di vista classico sia da un punto di vista quantistico. Termina la trattazione una descrizione delle tecniche di ricostruzione dell'immagine di RM e una descrizione delle singole componenti di uno strumento per MRI.

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scan, 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2, Supplement 1, June 2007, pp 357-359

A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at diff erent fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the infn (Istituto Nazionale Fisica Nucleare) research project gpcalma (Grid Platform for calma) which recruits physicists and radiologists from diff erent Italian Research Institutions and hospitals to develop software for breast and lung cancer detection.