Pompeii & Herculaneum archaeological sites: conservation and management

This first part of the paper begins with an historical overview of the development of Pompeii and Herculaneum as an archaeological site and heritage attraction, providing an insight concerning the culture and nature of these ancient citiesduring theRoman period. A focus will be given on the context of these rediscoveries, and their impact within Italy and around the world and most importantly who owned, managed, and/or visited the sites throughout their history.The paper also looks at how the ideas about thefame of Pompeii affects tourism, management, and the creation of UNESCO World Heritage Sites. The conclusion compares the potential of the recent public/private partnership initiative at Herculaneum ‘The Herculaneum Conservation Project’and the latest fully public funded project at Pompeii ‘The Great Project Pompeii’

The Effect of Maternal Diet on the Establishment and Development of the Offspring\u27s Gut Microbiome Studied in a Healthy Mouse Model

The mammalian gastrointestinal tract has a diverse community of microbial species, collectively referred to as the normal flora, that influence the health of the host. Early colonization of this location is essential for the development of a newborn as the presence of these microbes influence the establishment of the immune system, metabolism, and allergy development. Recent research suggests that the fetal gut is colonized in utero and can be influenced by maternal factors, such as diet. Therefore, the objective of this study was to determine if the diet of the mother alters the establishment and development of the offspring’s gut microbiome, reflected in a healthy mouse model. The ketogenic diet, characterized by high-fat and low-carbohydrates, and a standard pellet diet served as the experimental treatments. The fecal content of the offspring, born to mothers on each respective diet throughout pregnancy, was analyzed over a 32-week period to track the presence of particular bacterial species throughout development. This project identifies seven species as potential “core gut bacteria” that were used as markers of good health in monitoring the progression of the gut microbiome and changes in response to the ketogenic diet. Species-specific PCR primers were designed for each bacterium based on a unique target gene; however, time constraints prevented the resulting products from being analyzed. In the future, the amplified products from each species-specific PCR setup should be verified by gel electrophoresis and analyzed to determine the relative abundance of the seven bacteria

Identifying Galaxy Mergers in Observations and Simulations with Deep Learning

Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. With one of the main difficulties of merger studies being the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. A convolutional neural network architecture was developed and trained in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalizing prospects for using simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22 figures, 14 tables, 1 appendi

Deep Learning for Galaxy Mergers in the Galaxy Main Sequence

Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim. Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 91.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.Comment: 4 pages, 1 figure. For Proceedings IAU Symposium No. 34

Pathway to a Compact SASE FEL Device

Newly developed high peak power lasers have opened the possibilities of driving coherent light sources operating with laser plasma accelerated beams and wave undulators. We speculate on the combination of these two concepts and show that the merging of the underlying technologies could lead to new and interesting possibilities to achieve truly compact, coherent radiator devices

Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation

We consider two neuronal networks coupled by long-range excitatory interactions. Oscillations in the gamma frequency band are generated within each network by local inhibition. When long-range excitation is weak, these oscillations phase-lock with a phase-shift dependent on the strength of local inhibition. Increasing the strength of long-range excitation induces a transition to chaos via period-doubling or quasi-periodic scenarios. In the chaotic regime oscillatory activity undergoes fast temporal decorrelation. The generality of these dynamical properties is assessed in firing-rate models as well as in large networks of conductance-based neurons.Comment: 4 pages, 5 figures. accepted for publication in Physical Review Letter

Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $\sim100$ massive LRG-galaxy lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $\sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA

Histoplasmose pulmonar aguda e primeiro isolamento do Histoplasma capsulatum do solo do Rio Grande do Sul, Brasil

A case of acute pulmonary histoplasmosis, where the clinical histoiy and epidemiological data led to the identification of H. capsulatum natural source, is described. Specimens of spleen and liver, obtained after intraperitonial inoculation in mice, grew H. capsulatum in culture from the soil of rural area of General Câmara, by the first time in Rio Grande do Sul.Apresenta-se um caso de histoplasmose pulmonar aguda, onde a história clinica orientada levou a Identificação da fonte natural do Histoplasma capsulatum. O fungo foi obtido em cultivo a partir de fragmentos de baço e fígado de ratos inoculados intraperitonealmente com solo da zona rural de General Câmara, pela primeira vez no Rio Grande do Sul

Nanoparticles in Cancer Diagnosis and Treatment

The use of tailored medication delivery in cancer treatment has the potential to increase efficacy while decreasing unfavourable side effects. For researchers looking to improve clinical outcomes, chemotherapy for cancer continues to be the most challenging topic. Cancer is one of the worst illnesses despite the limits of current cancer therapies. New anticancer medications are therefore required to treat cancer. Nanotechnology has revolutionized medical research with new and improved materials for biomedical applications, with a particular focus on therapy and diagnostics. In cancer research, the application of metal nanoparticles as substitute chemotherapy drugs is growing. Metals exhibit inherent or surface-induced anticancer properties, making metallic nanoparticles extremely useful. The development of metal nanoparticles is proceeding rapidly and in many directions, offering alternative therapeutic strategies and improving outcomes for many cancer treatments. This review aimed to present the most commonly used nanoparticles for cancer applications