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The expansion of Integrated Pest Management (IPM), including biological control, has had several positive consequences for the agricultural environment and participants in the production chain. To enable successful operation and applications of biological control, production of insects used for rearing natural enemies (parasitoids and predators) must be optimized to reduce time and costs and improve production both qualitatively and quantitatively. The present study evaluated the effect of wheat varieties, the main component of artificial diets for Ephestia kuehniella, on the reproductive performance and biological parameters of this flour moth, which is used for mass production of Trichogramma spp. (Hymenoptera: Trichogrammatidae) and other parasitoids and predators. Four varieties of wheat were compared: BRS 327, BRS Marcante, BRS Parrudo, and KBR, with and without the addition of corn E. kuehniella reared on 97% BRS 327 wheat flour + 3% nutritional yeast had the best biological parameters and substitution of corn for about half of the wheat increased the number of eggs per female.

CMAM is a multidisciplinary-oriented scientific facility based on the operation of a 5 MV tandem ion accelerator and a suite of six beamlines, along with complementary infrastructures and scientific tools. This work describes CMAM, highlighting some of the present and future collaborations and standing out radiobiology (in particular Proton-therapy) as a strategic research line with high societal relevance. Proton-therapy has been, since the last thirty years, an alternative to conventional radio-treatment to heal or control radio-resistant cancers or tumours near sensitive organs such as spinal cord or brain tumours. The research in this field has a multidisciplinary approach since biologist, chemists and physicist must work together to detangle the different effects that are produced by the radiation. Flexible facilities such as CMAM may play a very relevant role in order to develop further basic knowledge and as a training field for students to this type of investigation. As an example of this, a preliminary radiobiology experiment is briefly described, in which a non-alive material with a similar composition than a cell culture was used to see the effects of the radiation and it was developed the basic methodologies for dose delivery and proton range control. The experiment was also used as a training practice for students in the framework of the INFIERI school conducted at Universidad Autónoma de Madrid in Summer 2021.

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.

In modern medical systems huge amount of text, words, images and videos are produced and stored in ad hoc databases. Medical community needs to extract precise information from that large amount of data. Currently ICT approaches do not provide a methodology for content-based medical images retrieval and classification. On the other hand, from the Internet of Things (IoT) perspective, the ICT medical data can be produced by several devices. Produced data complies with all Big Data features and constraints. The IoT guidelines put at the center of the system a new smart software to manage and transform Big Data in a new understanding form. This paper describes a text based indexing system for mammographic images retrieval and classification. The system deals with text (structured reports) and images (mammograms) mining and classification in a typical Department of Radiology. DICOM structured reports, containing free text for medical diagnosis, have been analyzed and labeled in order to classify the corresponding mammographic images. Information Retrieval process is based on some text manipulation techniques, such as light semantic analysis, stop-word removing, and light medical natural language processing. The system includes also a Search Engine module, based on a Bayes Naive Classifier. The experimental results provide interesting performance in terms of Specificity and Sensibility. Two more indexes have been computed in order to assess the system robustness: the A z (Area under ROC Curve) index and the σ A z ( A z standard error) index. The dataset is composed of healthy and pathological DICOM structured reports. Two use case scenarios are presented and described to prove the effectiveness of the proposed approach.

Oral Diseases (2012) 18, 396–401 Objective: The aim of this study is to assess the main oral mucosal lesions (OMLs) within a hospital base and to provide an anamnestic, diagnostic model based on homogeneity analysis of some variables. Methods: The demographic and behavioural data (i.e. gender, age, smoking status, alcohol consumption and therapeutic drug usage) of 1753 patients with at least one OML were considered. Multiple correspondence analysis (MCA) and multivariate tests of the simultaneous marginal homogeneity hypothesis (SMH) were used to analyse the evidence of any differences between the demographic and behavioural profiles relating to OMLs diagnoses. Statistical significance of P

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

The CALMA (Computer Assisted Library for MAmmography) project is a five years plan developed in a physics research frame in collaboration between INFN (Istituto Nazionale di Fisica Nucleare) and many Italian hospitals. At present a large database of digitized mammographic images (more than 6000) was collected and a software based on neural network algorithms for the search of suspicious breast lesions was developed. Two tools are available: a microcalcification clusters hunter, based on supervised and unsupervised feedforward neural network, and a massive lesions searcher, based on a hibrid approach. Both the algorithms analyzed preprocessed digitized images by high frequency filters. Clinical tests were performed to evaluate sensitivity and specificity of the system, considering the system as alone and as secon reader. Results show that the system is ready to be implemented by medical industry. The CALMA project, just ended, has its natural development in the GPCALMA (Grid Platform for CALMA) project, where distributed users join common resources (images, tools, statistical analysis)., Comment: 3 pages, 1 figure; to appear in the Proceedings of Frontier Detectors For Frontier Physics, 9th Pisa Meeting on Advanced Detectors, 25-31 May 2003, La Biodola, Isola d'Elba, Italy

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive),more » containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be A{sub z}=0.783{+-}0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.« less

The ectoparasitoid Bracon hebetor Say is an insect frequently found in storage facilities, where it attacks stored grain pests. The biology of this parasitoid was studied when reared on seven different artificial diets (in vitro rearing), under controlled temperature (25 ± 2 °C), relative humidity (60 ± 10%), and photoperiod (14-h photophase), and compared to its biology on its natural host Anagasta kuehniella (Zeller) (in vivo rearing). The artificial diet contained 60% holotissue of Diatraea saccharalis (Fabricius) pupae, 12% fetal bovine serum, 12% lactoalbumin hydrolysate, and 16% egg yolk, enabled development similar to that obtained on the natural host. The life cycle duration (egg–adult) was not significantly different, and the adults reared on this diet promptly paralyzed and parasitized the natural host, though at a lower proportion than those reared in vivo. There was no difference in the longevity of females obtained with these two different rearing systems (in vivo and in vitro). However, about 60% of the larvae developed on the diet failed to produce a protective cocoon during the pupal phase, indicating a sub-optimal quality associated with this artificial medium.

Ground state expectation values are obtained by using a path integral ground state Monte Carlo method. The method allows calculations of ground state expectation values without the extrapolations often used with Green’s function and diffusion Monte Carlo methods. We compare our results with those of Green’s function Monte Carlo by calculating some ground state properties of the van der Waals complex He2Cl2 as well as the infinite systems liquid and solid 4He. Advantages and disadvantages of the present method with respect to previous ones are discussed.

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.

Mass localization plays a crucial role in computer-aided detection CAD systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: a Image segmentation for the localization of regions of interest ROIs. This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. b ROI characterization by means of textural features computed from the gray tone spatial dependence matrix GTSDM, containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. c ROI classification by means of a neural network, with supervision provided by the radiologist’s diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images 2307 negative, 1062 pathological or positive, containing at least one confirmed mass, as diagnosed by an expert radiologist. To assess the performance of the system, receiver operating characteristic ROC and free-response ROC analysis were employed. The area under the ROC curve was found to be Az=0.783±0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

The objective of this study was to determine the prevalence of asthma-related symptoms in the Guadalajara Health Area, using the methods prescribed by the first phase of the European Asthma Study (EAS). A transversal descriptive study by self-administered questionnaire in a population of 2,968 subjects aged 20 to 44 years old assigned to the Guadalajara Health Area was performed. The study took place between September 1995 and February 1996. We received 2,435 responses (81.2% of the sample). Each symptom was studied in relation to sex, age, domicile and smoking and the results were compared to data from other Spanish health areas participating in the EAS. Significant differences were found between our results and those of other Spanish health areas. Subjects reporting asthma symptoms accounted for 8.6% (C.I. 7.5% to 9.8%) of the sample. Presence of asthma was related to place of residence but not to age or sex.

Medical community needs to extract precise information from a large amount of data. These data are a collection of different types such as text documents, images and video. Currently medical technology do not provide an intelligent methodology for documents recovery and classification of such documents based on their content. In this work the radiological structured reports are analysed with the corresponding mammographic images. The presented system is composed of an Indexing Engine and a Searching Engine, based on innovative methods for IR (Information Retrieval). The proposed work is useful for physicians as support diagnosis system, for students as learning support system, and finally, for epidemiological studies. The system allows for submitting a query through a web interface, consulting a structured report and showing associated mammographic images. The dataset is composed of both healthy and pathological patients. The proposed architecture achieved satisfactory results in terms of Sensitivity and Specificity. Three scenarios are presented and described. © 2013 IEEE.

In modern medical domain, documents are created directly in electronic form and stored on huge databases containing documents, text in integral form and images. Retrieving right informations from these servers is challenging and, sometimes, this is very time consuming. Current medical technology do not provide a smart methodology classification of such documents based on their content. In this work the radiological structured reports are analysed classified and assigning an appropriate label. The text classifier is used to label a mammographic structured report. The experimental data are real clinical report coming from a hospital server. Analysing the structured report content, the classifier labels the patient structured report as healthy or pathological. The present work uses Information Retrieval techniques to improve the classification process. These technique provide a light semantic analysis to remove negative terms, a removing stop-word step and, finally, a thesaurus is used to uniform used words. The structured reports are classified using a Bayes Naive Classifier. The experimental results provide interesting performance in terms of specificity and sensibility. Others two indexes are computed in order to assess system's robustness: these are the A z (Area under Curve ROC) and σ A z (A z standard error).

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass–Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.

Objective: The aim of this study is to assess the main oral mucosal lesions (OMLs) within a hospital base and to provide an anamnestic, diagnostic model based on homogeneity analysis of some variables. Methods: The demographic and behavioural data (i.e. gender, age, smoking status, alcohol consumption and therapeutic drug usage) of 1753 patients with at least one OML were considered. Multiple correspondence analysis (MCA) and multivariate tests of the simultaneous marginal homogeneity hypothesis (SMH) were used to analyse the evidence of any differences between the demographic and behavioural profiles relating to OMLs diagnoses. Statistical significance of P

The detection of microcalcifications is a hard task, since they are quite small and often poorly contrasted against the background of images. The Computer Aided Detection (CAD) systems could be very useful for breast cancer control. In this paper, we report a method to enhance microcalcifications cluster in digital mammograms. A Fuzzy Logic clustering algorithm with a set of features is used for clustering microcalcifications. The method described was tested on simulated clusters of microcalcifications, so that the location of the cluster within the breast and the exact number of microcalcifications is known.

Using variational and diffusion Monte Carlo techniques, we investigate the ground state of bosons interacting in the continuum through a repulsive modified-Bessel-function potential, [epsilon][ital K][sub 0]([ital r]/[sigma]), in two dimensions. This is a simplified model for flux lines in high-[ital T][sub [ital c]] superconductors. A pair-product trial function is first optimized so that its variational energy is very near the ground-state energy, then the diffusion Monte Carlo technqiue is used to calculate the exact ground-state energy. As a function of mass and density, we calculate the region of stability of the solid for densities greater than 0.01/[sigma][sup 2]. The quantum crystal melts at high density, due to the potential's soft core, and at low density, due to the exponentially weak interaction. Bosons with [h bar][sup 2]/2[ital m][sigma][sup 2][epsilon][gt]0.09 do not crystallize at any density. Within the flux model, we compute the flux-line phase diagram for Bi[sub 2]Sr[sub 2]CaCu[sub 2]O[sub 8]. Pair-correlation functions, structure factors, and Lindemann ratios at melting are also comptued.

The study compares the diagnostic accuracy (correct identification of cancer) of a new computer-assisted diagnosis (CAD) system (Cyclopus) with two other commercial systems (R2 and CADx). Cyclopus was tested on a set of 120 mammograms on which the two compared commercial systems had been previously tested. The set consisted of mammograms reported as negative, preceding 31 interval cancers reviewed as screening error or minimal sign, and of 89 verified negative controls randomly selected from the same screening database. Cyclopus sensitivity was 74.1% (R2=54.8%; CADx=41.9%) and was higher for interval cancers reviewed as screening error (90.9%; R2=54.5%; CADx=81.8%) compared with those reviewed as minimal sign (65.0%; R2=55.0%; CADx=20.0%). Specificity was 15.7% (R2=29.2%; CADx=17.9%). Overall accuracy was 30.8% (R2=35.8%; CADx=24.1%). The positive predictive value of a case with CAD marks [regions of interest (ROI)] was 23.4% (23/98; R2=16.0%; CADx=15.1%). Average ROI number per view among negative controls was 1.13 (R2=0.93; CADx=0.99). Cyclopus was more sensitive for masses compared with isolated microcalcifications (208 vs 62 ROI; R2=90 vs 213; CADx=192 vs 130). Compared with two other commercial systems, Cyclopus was more sensitive (R2 p=0.14; CADx p=0.02) and less specific (R2 p=0.02; CADx p=0.64). © 2009 Springer-Verlag Italia.

In this paper we propose a method to reduce the FP/imm number through CC and MLO mammographic views comparison of the same patient. The proposed solution uses the symmetry properties of the breast to compute a geometric transformation that permits to represent the two images in comparable coordinates systems. Through this method, potential pathological ROIs of one of the projections are correlated with the ROIs in the second view. To show the effectiveness of the result we apply the method on a dataset composed of 112 couples of pathological images. Experiments shows that method enables a reduction by up to 700/0 of the FP/imm number detected after the classification step

Breast cancer is the most widespread cancer in women in the world; it manifests mostly in two forms: microcalcifications and massive lesions. These two forms differ in density, size, shape and number. Consequently, there are two different kinds of mammographic CAD algorithms: those for microcalcifications detection, and those for massive lesions detection. The microcalcifications detection is a hard task, since they are quite small and often poorly contrasted against the background, especially in images affected by digitization noise. In a CAD system the ROI Hunter plays an important role, because missed microcalcifications at this level are definitely lost. For this reason, highlighting methods for suspected microcalcifications may be useful in a CAD system. In this work, we describe a Fourier Transform based microcalcifications enhancement method, which takes place after the image preprocessing and before the ROI Hunter step, aimed at highlighting suspected areas

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 based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% ± 1 of area under ROC curve obtained with the Feed Forward Neural Network., {"references":["Smith R.A., \"Epidemiology of breast cancer\", in \"A categorical course in\nphysics. Imaging considerations and medical physics responsibilities\",\nMadison, Winsconsin, Medical Physics Publishing, 1991.","Peto R., Boreham J., Clarke m., Davies c., Deral V, correspondence \"UK\nand USA Breast cancer deaths down 25% in year 2000 at ages 20-69\nyears\", LANCET 2000, 355, (9217) pp. 1822-1823, 2000.","Bird R., Wallace T., Yankaskas B., \"Analysis of cancer missed at\nscreening mammography\", Radiology 1992: 184; pp.613-617, 1992.","Bottigli U, Delogu P, Fantacci ME, Fauci F, Golosio B, Lauria A,\nPalmiero R, Raso G, Stumbo S, Tangaro S Search of Microcalcification\nclusters with the CALMA CAD station. The International Society for\nOptical Engineering (SPIE) 4684: 1301-1310, 2002","F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, S. C. Cheran, F. De Carlo,\nG. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E.Lopez Torres, R.\nMagro, G. L. Masala, P.Oliva, M. Quarta, G. Raso, A. Retico,\nS.Tangaro, Mammogram Segmentation by Contour Searching and\nMassive Lesion Classification with Neural Network, Proc. IEEE\nMedical Imaging Conference, October 16-22 2004, Rome, Italy; M2-\n373/1-5, 2004.","O. Duda, P. E. Hart, D. G. Stark, \"Pattern Classification\", second\nedition, A Wiley-Interscience Publication John Wiley & Sons, 2001.","S. J. Russel, P.Norvig, \"Artificial Intelligence. A modern approach\",\nUTET, 1998.","S. Haykin \"Neural Networks - A comprehensive foundation\", second\nedition, Prentice Hall, 1999.","V. N. Vapnik. \"Statistical Learning Theory. Wiley\", New York , 1998.\n[10] M. Pontil, A. Verri \"Properties of Support Vector Machines\", Neural\nComputation, Vol. 10, pp 955-974, 1998.\n[11] N. Cristianini, J. Shave-Taylor. \"An Introduction to Support Vector\nMachine\"(and other kernel-based learning methods). Cambridge\nUniversity Press 2000.\n[12] SVM_light software is available in the following location :\nftp://ftp-ai.cs.unidortmund.de/pub/Users/thorsten/svm_light/current/\nsvm_light.tar.gz\n[13] T. Joachims, Text Categorization with Support Vector Machines:\nLearning with Many Relevant Features, Proc. 10th European Conf.\nMachine Learning (ECML), Springer-Verlag, 1998.\n[14] T. Mitchell \"Machine Learning\" , McGraw-Hill 1997.\n[15] Timp S., Karssemeijer N., A new 2D segmentation method based on\ndynamic programming applied to computer aided detection in\nmammography, Medical Physics: 31; 958-971, 2004.\n[16] Baydush A.H., Catarious D.M., Abbey C.K., Floyd C.E., Computer\naided detection of masses in mammography using subregion Hotelling\nobservers, Medical Physics: 30; 1781-1787, 2003.\n[17] Tourassi G.D., Vargas-Voracek R., Catarious D.M. Jr, Floyd C.E. Jr,\nComputer-assisted detection of mammographic masses: A template\nmatching scheme based on mutual information, Medical Physics: 30 (8);\n2123-2130, 2003.\n[18] Antonie M.L., Zaiane O.R., Coman A., Application of data mining\ntechniques for medical image classification, Proc. of II Int. Work. On\nMultimedia Data Mining, USA, 2001.\n[19] Vyborny CJ., Giger ML., Computer vision and artificial intelligence in\nmammography, AJR: 162; 699-708, 1994.\n[20] Lai S., Li X., Bischof W., On techniques for detecting circumscribed\nmasses in mammograms\", IEEE Transaction on Medical Imaging: 8(4);\n377-386, 1989.\n[21] Hanley JA, McNeil B, The meaning and use of the area under a receiver\noperating characteristic (ROC) curve, Radiology: 143; 29-36, 1982.\n[22] Hanley JA, McNeil B, A method of comparing the areas under receiver\noperating characteristic curves derived from the same cases, Radiology:\n148; 839-843, 1983.\n[23] U. Bottigli, B. Golosio, G. L. Masala, P. Oliva, S. Stumbo, D. Cascio, F.\nFauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, S.Tangaro, I. De\nMitri, G. De Nunzio, M. Quarta, A. Preite Martinez, P. Cerello, S. C.\nCheran, E.Lopez Torres \"Dissimilarity Application for Medical Imaging\nClassification\" on proceedings of The 9th World Multi-Conference on\nSystemics, Cybernetics and Informatics WMSCI 2005, Orlando 10-13\nJuly 2005, vol III pag 258-262, 2005.\n[24] G. Masala, B. Golosio, D. Cascio, F. Fauci, S. Tangaro, M. Quarta, S. C\nCheran, E. L. Torres, \"Classifiers trained on dissimilarity representation\nof medical pattern: a comparative study\" on Nuovo Cimento C, Vol\n028, Issue 06, pp 905-912 , 2005."]}

O objetivo deste trabalho foi desenvolver uma dieta artificial para criação de Agrotis ipsilon em laboratório com base em parâmetros biológicos e na tabela de vida de fertilidade. A dieta artificial utilizada continha feijão, caseína, proteína de soja, levedura e germe de trigo como fontes protéicas. Os parâmetros biológicos duração e viabilidade das fases larval e pupal, peso de pupas, de ambos os sexos, com 24 horas de idade, razão sexual, longevidade dos adultos, período de pré-oviposição e número de ovos produzidos por fêmea e a tabela de vida de fertilidade foram avaliados. Foram observados seis ínstares larvais com duração de 25,4 dias e viabilidade de 93%. A duração da fase pupal foi de 12,4 dias e viabilidade de 96%. A viabilidade de ciclo total foi 72%. O peso de pupas foi 387 mg (machos) e 484 mg (fêmeas). A razão sexual foi 0,46. O período de pré-oviposição foi de um dia, com 1.806 ovos por fêmea. Na tabela de vida verificou-se que a taxa líquida de reprodução e a razão finita de aumento foram 616,9 e 1,14, respectivamente. A dieta artificial é adequada à manutenção da criação de A. ipsilon, em laboratório. The objective of this work was to develop an artificial diet to rear Agrotis ipsilon in laboratory using biological parameters and fertility life table. The artificial diet was prepared with bean, casein, soybean protein, yeast and wheat germ as protein sources. The biological aspects duration and viability of larval and pupal stages, pupal weight, sex ratio, life span of adults, preoviposition period, egg laying capacity and fertility life table were evaluated. Six larval instars were observed comprising larval duration of 28,4 days and 93% of viability mean pupal duration of 12,4 days and viability of 96%. The total viability of the life cycle was 72%. The pupae weight was 387 mg for males and 484 mg for females. The sex ratio was 0,46 and the preoviposition period lasted one day and egg laying was 1,806 eggs per female. The net reproductive rate per generation and increase finite rate were 616,9 and 1,14, respectively. Artificial diet is adequate for rearing A. ipsilon in laboratory.

Artificial diets prepared with wheat germ, soybean protein, dextrosol, potato starch, sucrose, cellulose, soybean or sunflower oil, and vitamin solution for rearing Nezara viridula (L.) and Euschistus heros (Fabricius) were tested under controlled temperature (25 +/- 1 degrees C), RH (60 +/- 10%), and photophase (14h). Three diets were tested and compared with the natural diet privet [soybean and peanut seeds and privet Ligustrum lucidum Ait. fruit (Oleaceae)]. All three artificial diets allowed full development. The diet containing sunflower oil was the most suitable for N. viridula while E. heros developed better on a diet composed of soybean oil. Data indicated that the artificial diets were inferior to the natural diet. The artificial diets were more adequate for E. heros.

Artificial diets prepared with wheat germ, soybean protein, dextrosol, potato starch, sucrose, cellulose, soybean or sunflower oil, and vitamin solution for rearing Nezara viridula (L.) and Euschistus heros (Fabricius) were tested under controlled temperature (25 ± 1°C), RH (60 ± 10%), and photophase (14h). Three diets were tested and compared with the natural diet privet [soybean and peanut seeds and privet Ligustrum lucidum Ait. fruit (Oleaceae)]. All three artificial diets allowed full development. The diet containing sunflower oil was the most suitable for N. viridula while E. heros developed better on a diet composed of soybean oil. Data indicated that the artificial diets were inferior to the natural diet. The artificial diets were more adequate for E. heros.

The biology of Bracon hebetor Say (Hymenoptera: Braconidae) reared on fifth instars of Anagasta kuehniella (Zeller) (Lepidoptera: Pyralidae) (natural diet) and in vitro (artificial diet) was evaluated. Data on the number of instars, development time and food intake were collected, and histochemical tests were conducted to detect proteins and lipids in the parasitoid's digestive tract. The data disclosed differences that can help to improve artificial rearing of B. hebetor. B. hebetor had three instars in both diets, but the developmental time on the artificial diets was prolonged due to the increase in larval and pupal development times. Larvae grew faster on the natural host and required a lower food intake (2.7 µl) as compared to that required by the larvae feeding on the artificial diet (3.8 µl). Analysis of diet protein content and host hemolymph and the observations on the parasitoid larvae gut content indicated altogether the artificial diets requires the addition of others sources of proteins and lipids to improve the overall nutrition quality of the in vitro rearing system for this ectoparasitoid. Avaliou-se a biologia de Bracon hebetor Say (Hymenoptera: Braconidae) criada em lagartas de 5º ínstar de Anagasta kuehniella (Zeller) (Lepidoptera: Pyralidae) (dieta natural) e em dieta artificial. Caracterizou-se o número de ínstares, o tempo de desenvolvimento e o consumo de alimento; testes histoquímicos permitiram observar a presença/ausência de lipídios e proteínas no trato digestivo do parasitóide. Tais estudos possibilitaram detectar diferenças para o aprimoramento da dieta artificial para a criação do parasitóide. B. hebetor apresentou três ínstares larvais em ambos os substratos alimentares, contudo o ciclo do parasitóide foi prolongado quando criado em dieta artificial, devido ao alongamento das fases de larva e pupa. B. hebetor consumiu aproximadamente 3,8 µl de dieta artificial e, considerando o mesmo fluxo de alimentação, aqueles criados sobre o hospedeiro ingeriram 2,7 µl (proporcionalmente em relação ao tempo e ao peso) e apresentaram ganho de peso mais rápido. Análises da hemolinfa do hospedeiro natural, da dieta artificial e do tubo digestivo de larvas de B. hebetor indicaram a necessidade de adição de outras fontes de lipídios e proteínas na dieta artificial, buscando aproximar o sistema in vitro daquele representado pelas lagartas hospedeiras (sistema in vivo).

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an 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 based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron., {"references": ["", "Smith R.A., \"Epidemiology of breast cancer\", in \"A categorical course in physics. Imaging considerations and medical physics responsibilities\", Madison, Wisconsin, Medical Physics Publishing, 1991.", "Peto R., Boreham J., Clarke m., Davies c., Deral V, correspondence \"UK and USA Breast cancer deaths down 25% in year 2000 at ages 20-69 years\", LANCET 2000, 355, (9217) pp. 1822-1823, 2000.", "Bird R., Wallace T., Yankaskas B., \"Analysis of cancer missed at screening mammography\", Radiology 1992: 184; pp.613-617, 1992.", "Haus A.G. and Yaffe M. (editors), \"A categorical course in physics. Technical aspects in breast imaging\". Radiological Society of North America, Presented at the 79th Scientific Assembly and Annual Meeting of RSNA, 1993.", "Feig S.A. , Yaffe M. , \"Digital mammography, computer aided diagnosis and telemammography\", Radiol. Clin. N. Am. 33, 1205-1230, 1995.", "Keddache S., Thilander-Klang A., Lanhede B. , \"Storage phosphor and film screen mammography: performance with different mammographic techniques\" Eur. Radiol. 9, 591-597, 1999.", "Schmidt R.A., Nishikawa R.M. , \"Clinical use of digital mammography: the presents and the perspects\", Digit. Imaging 8/1 suppl. 74-79, 1995.", "Karssemejer N., \"A stochastic method for automated detection of microcalcifications in digital mammograms\" in Information processing in medical imaging, Springer-Verlag New York, 227-238, 1991.", "Karssmejer N. , \"Reading screening mammograms with the help of neural networks\", Nederlands Tijdschriff geneeskd, 143/45, 2232-2236, 1999.\n[10] 10.C.J. Viborny, M.L. Giger, \"Computer vision and artificial intelligence in mammography\", AJR 162, 699-708, 1994.\n[11] Bottigli U, Delogu P, Fantacci ME, Fauci F, Golosio B, Lauria A, Palmiero R, Raso G, Stumbo S, Tangaro S Search of Microcalcification clusters with the CALMA CAD station. The International Society for Optical Engineering (SPIE) 4684: 1301-1310, 2002\n[12] F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, S. C. Cheran, F. De Carlo, G. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E.Lopez Torres, R. Magro, G. L. Masala, P.Oliva, M. Quarta, G. Raso, A. Retico, S.Tangaro, Mammogram Segmentation by Contour Searching and Massive Lesion Classification with Neural Network, Proc. IEEE Medical Imaging Conference, October 16-22 2004, Rome, Italy; M2- 373/1-5, 2004.\n[13] S. Haykin \"Neural Networks - A comprehensive foundation\", second edition, Prentice Hall, 1999.\n[14] Massimo Buscema & Semeion Group, \"Reti Neurali artificiali e sistemi sociali complessi\", volume 1 Teoria e modelli 1409.1, Franco Angeli, 1999.\n[15] S. Serpico, G. Vernazza, \"Teorie e tecniche del riconoscimento\", CUSL \"Il gabbiano\", 1997.\n[16] O. Duda, P. E. Hart, D. G. Stark, \"Pattern Classification\", second edition, A Wiley-Interscience Publication John Wiley & Sons, 2001.\n[17] S. J. Russel, P.Norvig, \"Artificial Intelligence. A modern approach\", UTET, 1998.\n[18] V. N. Vapnik. \"Statistical Learning Theory. Wiley\", New York , 1998.\n[19] M. Pontil, A. Verri \"Properties of Support Vector Machines\", Neural Computation, Vol. 10, pp 955-974, 1998.\n[20] N. Cristianini, J. Shave-Taylor. \"An Introduction to Support Vector Machine\"(and other kernel-based learning methods). Cambridge University Press 2000. [21] SVM_light software is available in the following location : ftp://ftp-ai.cs.unidortmund.de/pub/Users/thorsten/svm_light/current/ svm_light.tar.gz\n[22] T. Joachims, Text Categorization with Support Vector Machines: Learning with Many Relevant Features, Proc. 10th European Conf. Machine Learning (ECML), Springer-Verlag, 1998.\n[23] T. Mitchell \"Machine Learning\" , McGraw-Hill 1997.\n[24] Timp S., Karssemeijer N., A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography, Medical Physics: 31; 958-971, 2004.\n[25] Baydush A.H., Catarious D.M., Abbey C.K., Floyd C.E., Computer aided detection of masses in mammography using subregion Hotelling observers, Medical Physics: 30; 1781-1787, 2003.\n[26] Tourassi G.D., Vargas-Voracek R., Catarious D.M. Jr, Floyd C.E. Jr, Computer-assisted detection of mammographic masses: A template matching scheme based on mutual information, Medical Physics: 30 (8); 2123-2130, 2003.\n[27] Antonie M.L., Zaiane O.R., Coman A., Application of data mining techniques for medical image classification, Proc. of II Int. Work. On Multimedia Data Mining, USA, 2001.\n[28] Vyborny CJ., Giger ML., Computer vision and artificial intelligence in mammography, AJR: 162; 699-708, 1994.\n[29] Lai S., Li X., Bischof W., On techniques for detecting circumscribed masses in mammograms\", IEEE Transaction on Medical Imaging: 8(4); 377-386, 1989.\n[30] Aapo Hyv\u252c\u00bfarinen, Erkki Oja, \"Indipendent Component Analysis: Algorithms and Applications\", Neural Networks Research Centre, Helsinki University of Technology, Finland, \"Neural Networks\", 13 (4- 5):411-430, 2000.\n[31] Hanley JA, McNeil B, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology: 143; 29-36, 1982.\n[32] Hanley JA, McNeil B, A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology: 148; 839-843, 1983.\n[33] U. Bottigli, B. Golosio, G. L. Masala, P. Oliva, S. Stumbo, D. Cascio, F. Fauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, S.Tangaro, I. De Mitri, G. De Nunzio, M. Quarta, A. Preite Martinez, P. Cerello, S. C. Cheran, E.Lopez Torres \"Dissimilarity Application for Medical Imaging Classification\" on proceedings of The 9th World Multi-Conference on Systemics, Cybernetics and Informatics WMSCI 2005, Orlando 10-13 July 2005, vol III pag 258-262, 2005.\n[34] G. Masala, B. Golosio, D. Cascio, F. Fauci, S. Tangaro, M. Quarta, S. Cheran, E. L. Torres, “Classifiers trained on dissimilarity representation of medical pattern: a comparative study" on Nuovo Cimento C, Vol. 028, Issue 06, pp 905-912 , 2005."]}

In this work we present the results of a study about the impact of CAD tools on Sensitivity and Specificity in mammographic diagnosis. The approach is aimed to evaluate the statistical significance through the comparison of these figures of merit obtained in different situations. For this purpose two different CAD tools, the CALMA station (INFN project) and the SecondLook™ station (by CADx) have been used as a support for radiologists.

Purpose of this work is the development of an automatic system which could be useful for radiologists in the investigation of breast cancer. A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. GPCALMA (Grid Platform Computer Assisted Library for MAmmography), a collaboration among italian physicists and radiologists, has built a large distributed database of digitized mammographic images (at this moment about 5500 images corresponding to 1650 patients). This collaboration has developed a CAD (Computer Aided Detection) system which, installed in an integrated station, can also be used for digitization, as archive and to perform statistical analysis. With a GRID configuration it would be possible for the clinicians tele- and co-working in new and innovative groupings ('virtual organisations') and, using the whole database, by the GPCALMA tools several analysis can be performed. Furthermore the GPCALMA system allows to be abreast of the CAD technical progressing into several hospital locations always with remote working by GRID connection. We report in this work the results obtained by the GPCALMA CAD software implemented with a GRID connection., 6 pages, 4 figures, to appear in CARS 2003 Proceedings, Computer Assisted Radiology and Surgery 17th International Congress and Exhibition, London, June 25-28, 2003

The GPCALMA (Grid Platform for Computer Assisted Library for MAmmography) collaboration involves several departments of physics, INFN sections, and italian hospitals. The aim of this collaboration is developing a tool that can help radiologists in early detection of breast cancer. GPCALMA has built a large distributed database of digitised mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) software which is integrated in a station that can also be used for acquire new images, as archive and to perform statistical analysis. The images are completely described: pathological ones have a consistent characterization with radiologist's diagnosis and histological data, non pathological ones correspond to patients with a follow up at least three years. The distributed database is realized throught the connection of all the hospitals and research centers in GRID tecnology. In each hospital local patients digital images are stored in the local database. Using GRID connection, GPCALMA will allow each node to work on distributed database data as well as local database data. Using its database the GPCALMA tools perform several analysis. A texture analysis, i.e. an automated classification on adipose, dense or glandular texture, can be provided by the system. GPCALMA software also allows classification of pathological features, in particular massive lesions analysis and microcalcification clusters analysis. The performance of the GPCALMA system will be presented in terms of the ROC (Receiver Operating Characteristic) curves. The results of GPCALMA system as "second reader" will also be presented., Comment: 6 pages, Proceedings of the Seventh Mexican Symposium on Medical Physics 2003, Vol. 682/1, pp. 67-72, Mexico City, Mexico

Mullerian duct anomalies (MDAs) are congenital anomalies of the internal female genital tract that result from non-development or non-fusion of the Mullerian ducts or failure of resorption of the u...

Bracon hebetor Say, 1857 é um ectoparasitóide larval de várias espécies de piralídeos que atacam grãos armazenados, sendo considerado um agente potencial de controle biológico dessas pragas. Assim, a biologia de B. hebetor foi estudada em condições controladas de temperatura (25 ± 2°C), umidade relativa (60 ± 10%) e fotoperíodo (fotofase de 14 horas), sobre sete espécies de lepidópteros, Anagasta kuehniella (Zeller), Corcyra cephalonica Stainton e Sitotroga cerealella (Olivier) (hospedeiros naturais) e Diatraea saccharalis (Fabricius), Heliothis virescens (Fabricius), Anticarsia gemmatalis Hübner, Spodoptera frugiperda (J.E. Smith) (insetos criados rotineiramente no laboratório de Biologia de Insetos da ESALQ/USP), para determinar o melhor hospedeiro para sua criação em laboratório. A duração e a viabilidade do ciclo biológico (ovo-adulto) foram afetadas, dependendo do hospedeiro utilizado. A. kuehniella e C. cephalonica foram hospedeiros semelhantes para a criação de B. hebetor, produzindo insetos com duração de ciclo de 12,8 dias, viabilidade em torno de 90% e ótima capacidade de paralisação e parasitismo. D. saccharalis foi o único hospedeiro não natural que proporcionou resultados semelhantes àqueles obtidos sobre as três traças hospedeiras naturais. Quanto à agressividade, D. saccharalis produziu insetos com capacidade de paralisação e parasitismo semelhantes àqueles criados sobre as traças S. cerealella e C. cephalonica. Bracon hebetor Say, 1857 is a larval ectoparasitoid of several species of pyralids which attack stored grains, therefore a potential biological control agent of these pests. To determine the best host for laboratory rearing, the biology of B. hebetor was studied under controlled temperature conditions (25 ± 2°C), relative humidity (60 ± 10%) and photoperiod (14-hour photophase) on seven lepidopteran larvae: natural hosts Anagasta kuehniella (Zeller), Corcyra cephalonica Stainton and Sitotroga cerealella (Olivier) and in the insects regularly reared at the Insect Biology laboratory of ESALQ/USP, Diatraea saccharalis (Fabricius), Heliothis virescens (Fabricius), Anticarsia gemmatalis Hübner, and Spodoptera frugiperda (J.E. Smith). Duration and viability of the biological cycle (egg-adult) were affected according to the host used. A. kuehniella and C. cephalonica were similar hosts for B. hebetor rearing, producing insects with life cycle of 12.8 days, viability around 90% and excellent paralyzing and parasitism capacities. D. saccharalis was the only not-natural host providing similar results to those found on the three-grain stored moths. Regarding aggressiveness, D. saccharalis produced insects with paralyzing and parasitism capacities similar to those reared on S. cerealella and C. cephalonica.

Modifications have been made to the FORTRAN source code of the EXTRAN block of SWMM, which enable the model to take advantage of parallel processors for faster…

To determine whether tobacco consumption affects the prevalence of asthmatic symptoms. The European Asthma Study Questionnaire was used.Cross-sectional descriptive study.Guadalajara Health Area.Sample of 3000 people between 20 and 44 living in Guadalajara.None.Measuring instrument was the European Asthma Study Questionnaire, to which two items on tobacco dependency were added. All the symptoms except asthma attack changed in function of the amount of tobacco consumed. Sibilant noises varied from 8.3% in non-smokers (NS), to 17.5% in light smokers (LS), 28.7% in moderate smokers (MS) and 42.6% in heavy smokers (HS). Night cough also increased in parallel with consumption: 14.1% in NS, 23.2% in LS, 41.2% in HS. Nocturnal thoracic pressure (14.7%), attacks of breathlessness (12.3%) and being considered to have asthma symptoms (14.2%) were more common in heavy smokers. Non-smokers had greater prevalence of sibilant noise with breathlessness (NS 57.7%, LS 39.2%, MS 30.4%, HS 34.5%) and without an associated cold (NS 45.2%, LS 20.3%, MS 23.4%, HS 29.9%), and used asthma treatments more often (NS 3.5%, LS 1.7%, MS 1.5%, HS 1%).The studies using this questionnaire should include additional questions on the tobacco history of the participants, since it seems that tobacco smoking and its amount may affect findings.

The objective of this study was to determine the prevalence of asthma-related symptoms in the Guadalajara Health Area, using the methods prescribed by the first phase of the European Asthma Study (EAS). A transversal descriptive study by self-administered questionnaire in a population of 2,968 subjects aged 20 to 44 years old assigned to the Guadalajara Health Area was performed. The study took place between September 1995 and February 1996. We received 2,435 responses (81.2% of the sample). Each symptom was studied in relation to sex, age, domicile and smoking and the results were compared to data from other Spanish health areas participating in the EAS. Significant differences were found between our results and those of other Spanish health areas. Subjects reporting asthma symptoms accounted for 8.6% (C.I. 7.5% to 9.8%) of the sample. Presence of asthma was related to place of residence but not to age or sex.

The equation of state of hydrogen plasma is calculated by the restricted path integral Monte Carlo method. We have investigated the plasma from the classical weak coupling regime to the quantum strongly coupled regime. Good agreement is found with the existing theories for low electronic degeneracy. Inception of molecular formation is observed at low densities and temperatures.

Description and quantification of self medication in a general paediatric office.Descriptive and prospective study. SITE. Cervantes Health Centre (Guadalajara).387 cases of both sexes until 8 that attended to on demand consultation during the period of two months.36% of the patients were given drugs before attending to consultation and in 18.7% of these cases, the actuation was incorrect. Parents were, mainly the responsible of incorrect treatment. The most often registered pathology was common cold. We did not found relation between self medication and the next variables: child's age or sex, parents' age or studies, number of children in the family, number of consultation for the same pathologic event and mother working outside home.Self medication is a common practice. Parents should be instructed about management of the commonest pathologies and when they should ask the doctor.

We comment on the recent letter by Wang et al. [Opt. Lett. 34, 1955 (2009)], in which the authors presented a high-speed digital image correlation (DIC) method. We consider that the so-called high-speed DIC method has considerable deficiencies and that the Letter is misleading in terms of applicability and measurement accuracy as well as processing speed.