Advances in the field of nanooncology

Nanooncology, the application of nanobiotechnology to the management of cancer, is currently the most important chapter of nanomedicine. Nanobiotechnology has refined and extended the limits of molecular diagnosis of cancer, for example, through the use of gold nanoparticles and quantum dots. Nanobiotechnology has also improved the discovery of cancer biomarkers, one such example being the sensitive detection of multiple protein biomarkers by nanobiosensors. Magnetic nanoparticles can capture circulating tumor cells in the bloodstream followed by rapid photoacoustic detection. Nanoparticles enable targeted drug delivery in cancer that increases efficacy and decreases adverse effects through reducing the dosage of anticancer drugs administered. Nanoparticulate anticancer drugs can cross some of the biological barriers and achieve therapeutic concentrations in tumor and spare the surrounding normal tissues from toxic effects. Nanoparticle constructs facilitate the delivery of various forms of energy for noninvasive thermal destruction of surgically inaccessible malignant tumors. Nanoparticle-based optical imaging of tumors as well as contrast agents to enhance detection of tumors by magnetic resonance imaging can be combined with delivery of therapeutic agents for cancer. Monoclonal antibody nanoparticle complexes are under investigation for diagnosis as well as targeted delivery of cancer therapy. Nanoparticle-based chemotherapeutic agents are already on the market, and several are in clinical trials. Personalization of cancer therapies is based on a better understanding of the disease at the molecular level, which is facilitated by nanobiotechnology. Nanobiotechnology will facilitate the combination of diagnostics with therapeutics, which is an important feature of a personalized medicine approach to cancer

An automated high-content screening image analysis pipeline for the identification of selective autophagic inducers in human cancer cell lines.

Automated image processing is a critical and often rate-limiting step in high-content screening (HCS) workflows. The authors describe an open-source imaging-statistical framework with emphasis on segmentation to identify novel selective pharmacological inducers of autophagy. They screened a human alveolar cancer cell line and evaluated images by both local adaptive and global segmentation. At an individual cell level, region-growing segmentation was compared with histogram-derived segmentation. The histogram approach allowed segmentation of a sporadic-pattern foreground and hence the attainment of pixel-level precision. Single-cell phenotypic features were measured and reduced after assessing assay quality control. Hit compounds selected by machine learning corresponded well to the subjective threshold-based hits determined by expert analysis. Histogram-derived segmentation displayed robustness against image noise, a factor adversely affecting region growing segmentation

Implementing textural features on GPUs for improved real-time pavement distress detection

The condition of municipal roads has deteriorated considerably in recent years, leading to large scale pavement distress such as cracks or potholes. In order to enable road maintenance, pavement distress should be timely detected. However, manual investigation, which is still the most widely applied approach toward pavement assessment, puts maintenance personnel at risk and is time-consuming. During the last decade, several efforts have been made to automatically assess the condition of the municipal roads without any human intervention. Vehicles are equipped with sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface. Yet, this data are usually not processed while driving, but instead it is recorded and later analyzed off-line. As a result, a vast amount of memory is required to store the data and the available memory may not be sufficient. To reduce the amount of saved data, the authors have previously proposed a graphics processing units (GPU)-enabled pavement distress detection approach based on the wavelet transform of pavement images. The GPU implementation enables pavement distress detection in real time. Although the method used in the approach provides very good results, the method can still be improved by incorporating pavement surface texture characteristics. This paper presents an implementation of textural features on GPUs for pavement distress detection. Textural features are based on gray-tone spatial dependencies in an image and characterize the image texture. To evaluate the computational efficiency of the GPU implementation, performance tests are carried out. The results show that the speedup achieved by implementing the textural features on the GPU is sufficient to enable real-time detection of pavement distress. In addition, classification results obtained by applying the approach on 16,601 pavement images are compared to the results without integrating textural features. There results demonstrate that an improvement of 27% is achieved by incorporating pavement surface texture characteristics

Spectroscopic investigation of quantum confinement effects in ion implanted silicon-on-sapphire films

Crystalline Silicon-on-Sapphire (SOS) films were implanted with boron (B$^+$) and phosphorous (P$^+$) ions. Different samples, prepared by varying the ion dose in the range $10^{14}$ to 5 x $10^{15}$ and ion energy in the range 150-350 keV, were investigated by the Raman spectroscopy, photoluminescence (PL) spectroscopy and glancing angle x-ray diffraction (GAXRD). The Raman results from dose dependent B$^+$ implanted samples show red-shifted and asymmetrically broadened Raman line-shape for B$^+$ dose greater than $10^{14}$ ions cm$^{-2}$. The asymmetry and red shift in the Raman line-shape is explained in terms of quantum confinement of phonons in silicon nanostructures formed as a result of ion implantation. PL spectra shows size dependent visible luminescence at $\sim$ 1.9 eV at room temperature, which confirms the presence of silicon nanostructures. Raman studies on P$^+$ implanted samples were also done as a function of ion energy. The Raman results show an amorphous top SOS surface for sample implanted with 150 keV P$^+$ ions of dose 5 x $10^{15}$ ions cm$^{-2}$. The nanostructures are formed when the P$^+$ energy is increased to 350 keV by keeping the ion dose fixed. The GAXRD results show consistency with the Raman results.Comment: 9 Pages, 6 Figures and 1 Table, \LaTex format To appear in SILICON(SPRINGER

The Schro¨\ddot{o}dinger-Poisson equations as the large-N limit of the Newtonian N-body system: applications to the large scale dark matter dynamics

In this paper it is argued how the dynamics of the classical Newtonian N-body system can be described in terms of the Schr$\ddot{o}$dinger-Poisson equations in the large $N$ limit. This result is based on the stochastic quantization introduced by Nelson, and on the Calogero conjecture. According to the Calogero conjecture, the emerging effective Planck constant is computed in terms of the parameters of the N-body system as $\hbar \sim M^{5/3} G^{1/2} (N/)^{1/6}$, where is $G$ the gravitational constant, $N$ and $M$ are the number and the mass of the bodies, and $$ is their average density. The relevance of this result in the context of large scale structure formation is discussed. In particular, this finding gives a further argument in support of the validity of the Schr$\ddot{o}$dinger method as numerical double of the N-body simulations of dark matter dynamics at large cosmological scales.Comment: Accepted for publication in the Euro. Phys. J.

Implementación de un prototipo funcional de aprendizaje de máquina para identificar correos electrónicos de Spear Phishing

Trabajo de investigaciónEste trabajo tiene como propósito la detección de correos electrónicos Spear Phishing a mediante un prototipo web, debido a que las técnicas de ingeniería social son muy usadas hoy en día para robar a los usuarios datos de identidad personal y/o credenciales de sus cuentas financieras, por esta razón, todas las personas deben implementar una medida para detectar estos ataques de ingeniería social.2 JUSTIFICACIÓN 3 PLANTEAMIENTO DEL PROBLEMA 4 OBJETIVOS 5 MARCOS DE REFERENCIA 6 ESTADO DEL ARTE 7 METODOLOGÍA 8 DESARROLLO DE LA PROPUESTA 9 INSTALACIÓN Y EQUIPO REQUERIDO 10 RESULTADOS 11 CONCLUSIONES 12 TRABAJOS FUTUROS 13 BIBLIOGRAFÍA 14 ANEXOSPregradoIngeniero de Sistema

Contact lens rehabilitation following repaired corneal perforations

BACKGROUND: Visual outcome following repair of post-traumatic corneal perforation may not be optimal due to presence of irregular keratometric astigmatism. We performed a study to evaluate and compare rigid gas permeable contact lens and spectacles in visual rehabilitation following perforating corneal injuries. METHOD: Eyes that had undergone repair for corneal perforating injuries with or without lens aspiration were fitted rigid gas permeable contact lenses. The fitting pattern and the improvement in visual acuity by contact lens over spectacle correction were noted. RESULTS: Forty eyes of 40 patients that had undergone surgical repair of posttraumatic corneal perforations were fitted rigid gas permeable contact lenses for visual rehabilitation. Twenty-four eyes (60%) required aphakic contact lenses. The best corrected visual acuity (BCVA) of ≥ 6/18 in the snellen's acuity chart was seen in 10 (25%) eyes with spectacle correction and 37 (92.5%) eyes with the use of contact lens (p < 0.001). The best-corrected visual acuity with spectacles was 0.20 ± 0.13 while the same with contact lens was 0.58 ± 0.26. All the patients showed an improvement of ≥ 2 lines over spectacles in the snellen's acuity chart with contact lens. CONCLUSION: Rigid gas permeable contact lenses are better means of rehabilitation in eyes that have an irregular cornea due to scars caused by perforating corneal injuries

Nanotechnology in Head and Neck Cancer: The Race Is On

Rapid advances in the ability to produce nanoparticles of uniform size, shape, and composition have started a revolution in the sciences. Nano-sized structures herald innovative technology with a wide range of potential therapeutic and diagnostic applications. More than 1000 nanostructures have been reported, many with potential medical applications, such as metallic-, dielectric-, magnetic-, liposomal-, and carbon-based structures. Of these, noble metallic nanoparticles are generating significant interest because of their multifunctional capacity for novel methods of laboratory-based diagnostics, in vivo clinical diagnostic imaging, and therapeutic treatments. This review focuses on recent advances in the applications of nanotechnology in head and neck cancer, with special emphasis on the particularly promising plasmonic gold nanotechnology

Commonality Preserving Multiple Instance Clustering Based on Diverse Density

Abstract. Image-set clustering is a problem decomposing a given im-age set into disjoint subsets satisfying specied criteria. For single vector image representations, proximity or similarity criterion is widely applied, i.e., proximal or similar images form a cluster. Recent trend of the im-age description, however, is the local feature based, i.e., an image is described by multiple local features, e.g., SIFT, SURF, and so on. In this description, which criterion should be employed for the clustering? As an answer to this question, this paper presents an image-set clus-tering method based on commonality, that is, images preserving strong commonality (coherent local features) form a cluster. In this criterion, image variations that do not affect common features are harmless. In the case of face images, hair-style changes and partial occlusions by glasses may not affect the cluster formation. We dened four commonality mea-sures based on Diverse Density, that are used in agglomerative clustering. Through comparative experiments, we conrmed that two of our meth-ods perform better than other methods examined in the experiments.

Online dietary intake estimation : The food4me food frequency questionnaire

Copyright ©Hannah Forster, Rosalind Fallaize, Caroline Gallagher, Clare B O’Donovan, Clara Woolhead, Marianne C Walsh, Anna L Macready, Julie A Lovegrove, John C Mathers, Michael J Gibney, Lorraine Brennan, Eileen R Gibney. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.06.2014. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Dietary assessment methods are important tools for nutrition research. Online dietary assessment tools have the potential to become invaluable methods of assessing dietary intake because, compared with traditional methods, they have many advantages including the automatic storage of input data and the immediate generation of nutritional outputs. Objective: The aim of this study was to develop an online food frequency questionnaire (FFQ) for dietary data collection in the Food4Me study and to compare this with the validated European Prospective Investigation of Cancer (EPIC) Norfolk printed FFQ. Methods: The Food4Me FFQ used in this analysis was developed to consist of 157 food items. Standardized color photographs were incorporated in the development of the Food4Me FFQ to facilitate accurate quantification of the portion size of each food item. Participants were recruited in two centers (Dublin, Ireland and Reading, United Kingdom) and each received the online Food4Me FFQ and the printed EPIC-Norfolk FFQ in random order. Participants completed the Food4Me FFQ online and, for most food items, participants were requested to choose their usual serving size among seven possibilities from a range of portion size pictures. The level of agreement between the two methods was evaluated for both nutrient and food group intakes using the Bland and Altman method and classification into quartiles of daily intake. Correlations were calculated for nutrient and food group intakes. Results: A total of 113 participants were recruited with a mean age of 30 (SD 10) years (40.7% male, 46/113; 59.3%, 67/113 female). Cross-classification into exact plus adjacent quartiles ranged from 77% to 97% at the nutrient level and 77% to 99% at the food group level. Agreement at the nutrient level was highest for alcohol (97%) and lowest for percent energy from polyunsaturated fatty acids (77%). Crude unadjusted correlations for nutrients ranged between .43 and .86. Agreement at the food group level was highest for other fruits (eg, apples, pears, oranges) and lowest for cakes, pastries, and buns. For food groups, correlations ranged between .41 and .90. Conclusions: The results demonstrate that the online Food4Me FFQ has good agreement with the validated printed EPIC-Norfolk FFQ for assessing both nutrient and food group intakes, rendering it a useful tool for ranking individuals based on nutrient and food group intakes.Peer reviewedFinal Published versio