Air fungal contamination in ten hospitals’ food units from Lisbon

A descriptive study was developed to monitor air fungal contamination in ten food units from hospitals. Fifty air samples of 250 litres were collected through impaction method. Samples were collected in food storage facilities, kitchen, food plating, canteen and also, outside premises, since this is the place regarded as reference. Simultaneously, environmental parameters were also monitored, including temperature and relative humidity through the equipment Babouc, LSI Sistems and according to the International Standard ISO 7726.

Assessment of fungal contamination in a Portuguese maternity unit

A descriptive study was developed to monitor air fungal contamination in one Portuguese maternity. Sixty air samples were collected through impaction method. Air sampling was performed in food storage facilities, kitchen, food plating, canteen, pharmacy, sterilization areas, genecology wards, intensive care unit, operating rooms, urgency and also, outside premises, since this was the place regarded as reference. Besides air samples, forty three samples were collected by swabbing the surfaces using a 10 by 10 cm square stencil. Simultaneously, temperature, relative humidity and particles counting (PM10) were registered. Twenty three species of fungi were identified in air, being the two most commonly isolated the genera Penicillium (41,5%) and Cladosporium (28,4%). Regarding yeasts, only Rhodotorula sp. (45,2%), Trichosporon mucoides (51,6%) and Cryptococcus neoformans (3,2%) were found. Thirteen species of fungi were identified in surfaces, being the most frequent the Penicillium genus (91,6%). Concerning yeasts found in surfaces, four species were identified being Rhodotorula sp. (29,1%) the most frequent. There was no coincidence between prevailing genera indoors and outside premises. Moreover, some places presented fungal species different from the ones isolated outside. In the inside environment, Aspergillus species were isolated in air and surfaces. There was no significant relationship (p>0,05) between fungal contamination and the studied environmental variables. Keywords: air, surfaces, fungal contamination, environmental variables, maternity.

A Comparison of Geo-tagging in Mobile Internet Browsing Applications on iOS and Android.

Nowadays there is almost no crime committed without a trace of digital evidence, and since the advanced functionality of mobile devices today can be exploited to assist in crime, the need for mobile forensics is imperative. Many of the mobile applications available today, including internet browsers, will request the user’s permission to access their current location when in use. This geolocation data is subsequently stored and managed by that application's underlying database files. If recovered from a device during a forensic investigation, such GPS evidence and track points could hold major evidentiary value for a case. The aim of this paper is to examine and compare to what extent geolocation data is available from the iOS and Android operating systems. We focus particularly on geolocation data recovered from internet browsing applications, comparing the native Safari and Browser apps with Google Chrome, downloaded on to both platforms. All browsers were used over a period of several days at various locations to generate comparable test data for analysis. Results show considerable differences not only in the storage locations and formats, but also in the amount of geolocation data stored by different browsers and on different operating systems.

Effects of gamma radiation in mycotoxin descontamination of Aloysia citrodora Paláu

The interest and demand for aromatic and medicinal plants have been growing due to their combined organoleptic and bioactive properties. However, in general these plants suffer natural contamination by fungi and associated toxins during growth as also in harvesting, storage and drying processes, which represents a threat to public health. The rigorous standards required by the industrial sector in terms of good quality of raw materials demand efficient decontamination procedures (1-3). Gamma radiation is assumed as an accredited methodology for the decontamination of medicinal and aromatic plants, with numerous advantages not only to the product itself but also to the consumer and the environment (4). In this study, efficient methods for detecting aflatoxins (AFB" AFB2, AFG1 and AFG2) and ocratoxin A (OTA), were optimized and validated, and afterwards, applied to spiked samples of Aloysia citrodora Pahiu submitted to gamma radiation treatment at different doses (I , 5 and I 0 kGy ), to evaluate the effectiveness of irradiation as a decontamination technique for dry plants. Mycotoxin levels were determined by reversed-phase high-performance liquid chromatography (HPLC) with fluorescence detection, after immunoaffinity column (lAC) cleanup. All the applied gamma radiation doses conducted to a degradation of the studied mycotoxins. In relation to the control sample (0 kGy), the reduction rates in the irradiated samples ranged from 4.9 and 5.2% in OTA, 5.3 and 9.6% in AFBt. 12.3 and 13.5 in AFB2, 16.4 and 23.6 in AFG1 and, finally, 52.6 and 62.7% in AFG2. The gamma radiation dose of 5 kGy stood out as the best decontamination dose for AFB1 and AFG1, which are the most significant aflatoxins naturally found in food commodities. For OTA, AFG2 and AFB2 there was no significant difference in decontamination between doses. In conclusion, the extraction and analysis methods proved to be suitable for detection of aflatoxins and ocratoxin A in A. citrodora. Gamma radiation seems to be an effective technique for reducing aflatoxins G in A. citrodora, and eventually oth~r medicinal and aromatic plants. On the other hand, aflatoxins B and OTA are less affected by this treatment.

A novel mutation of AFG3L2 might cause dominant optic atrophy in patients with mild intellectual disability

International audience

Plant Migrations Impact on Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change

Forests have a prominent role in carbon storage and sequestration. Anthropogenic forcing has the potential to accelerate climate change and alter the distribution of forests. How forests redistribute spatially and temporally in response to climate change can alter their carbon sequestration potential. The driving question for this research was: How does plant migration from climate change impact vegetation distribution and carbon sequestration potential over continental scales? Large-scale simulation of the equilibrium response of vegetation and carbon from future climate change has shown relatively modest net gains in sequestration potential, but studies of the transient response has been limited to the sub-continent or landscape scale. The transient response depends on fine scale processes such as competition, disturbance, landscape characteristics, dispersal, and other factors, which makes it computational prohibitive at large domain sizes. To address this, this research used an advanced mechanistic model (Ecosystem Demography Model, ED) that is individually based, but pseudo-spatial, that reduces computational intensity while maintaining the fine scale processes that drive the transient response. First, the model was validated against remote sensing data for current plant functional type distribution in northern North America with a current climatology, and then a future climatology was used to predict the potential equilibrium redistribution of vegetation and carbon from future climate change. Next, to enable transient calculations, a method was developed to simulate the spatially explicit process of dispersal in pseudo-spatial modeling frameworks. Finally, the new dispersal sub-model was implemented in the mechanistic ecosystem model, and a model experimental design was designed and completed to estimate the transient response of vegetation and carbon to climate change. The potential equilibrium forest response to future climate change was found to be large, with large gross changes in distribution of plant functional types and comparatively smaller changes in net carbon sequestration potential for the region. However, the transient response was found to be on the order of centuries, and to depend strongly on disturbance rates and dispersal distances. Future work should explore the impact of species-specific disturbance and dispersal rates, landscape fragmentation, and other processes that influence migration rates and have been simulated at the sub-continent scale, but now at continental scales, and explore a range of alternative future climate scenarios as they continue to be developed.

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

The Impact of Regulation on a Firm's Incentives to Invest in Emergent Smart Grid Technologies

This paper analyzes the implementation of new technologies in network industries through the development of a suitable regulatory scheme. The analysis focuses on Smart Grid (SG) technologies which, among others benefits, could save operational costs and reduce the need for further conventional investments in the grid. In spite of the benefits that may result from their implementation, the adoption of SGs by network operators can be hampered by the uncertainties surrounding actual performances. A decision model has been developed to assess the firms' incentives to invest in "smart" technologies under different regulatory schemes. The model also enables testing the impact of uncertainties on the reduction of operational costs, and of conventional investments. Under certain circumstances, it may be justified to support the development and early deployment of emerging innovations that have a high potential to ameliorate the efficiency of the electricity system, but whose adoption faces many uncertainties.

Impact of single parameter changes on Ceph cloud storage performance

In a general purpose cloud system efficiencies are yet to be had from supporting diverse applications and their requirements within a storage system used for a private cloud. Supporting such diverse requirements poses a significant challenge in a storage system that supports fine grained configuration on a variety of parameters. This paper uses the Ceph distributed file system, and in particular its global parameters, to show how a single changed parameter can effect the performance for a range of access patterns when tested with an OpenStack cloud system.

Revisiting compressed air energy storage.

The use of renewable energies as a response to the EU targets defined for 2030 Climate Change and Energy has been increasing. Also non-dispatchable and intermittent renewable energies like wind and solar cannot generally match supply and demand, which can also cause some problems in the grid. So, the increased interest in energy storage has evolved and there is nowadays an urgent need for larger energy storage capacity. Compressed Air Energy Storage (CAES) is a proven technology for storing large quantities of electrical energy in the form of high-pressure air for later use when electricity is needed. It exists since the 1970’s and is one of the few energy storage technologies suitable for long duration (tens of hours) and utility scale (hundreds to thousands of MW) applications. It is also one of the most cost-effective solutions for large to small scale storage applications. Compressed Air Energy Storage can be integrated and bring advantages to different levels of the electric system, from the Generation level, to the Transmission and Distribution levels, so in this paper a revisit of CAES is done in order to better understand what and how it can be used for our modern needs of energy storage.

Postharvest treatments to mitigate the internal browning in "Bartlett" pears.

Internal browning is an important disorder in pear fruit which can lead to economic losses. Pears (Pyrus communis L. cv. Bartlett) were harvested at early harvest maturity of 90 N from a commercial orchard in southern Brazil. Methyl jasmonate, ethanol, and 1-methylcyclopropene vapor treatments were carried out for 24 hours in order to mitigate the internal browning disorder. Fruit were stored for up to 150 days at 0 ± 1 °C and 90 ± 5 % RH. Pears exhibited internal browning in 37 % of the control samples after 90 days of cold storage. However, no internal browning symptoms were observed in the 1-MCP treatment. The first symptoms in 1-MCP samples were noticed after 120 days of cold storage (12 %) and reached 100 % in five days at room temperature. 1-MCP-treated pears showed flesh firmness values of 82 N after 90 days of cold storage and 18.7 N when they were removed from the cold storage and kept at 20 °C. The greatest acceptance index was attributed to 1- MCP pears after 90 days at 0 ± 1 °C followed by 5 days at 20 ± 1 °C (89.35). High acceptance indexes were attributed to MeJa (77.95) and control pears (76.40) after 30 days in cold storage followed by 5 days at room temperature. 1-MCP (0.3 µL L-1 , 24 hours at 0 ± 1 °C) treatment delays ripening and mitigates the internal browning in early harvested ?Bartlett? pears, that can be stored for up to 90 days at 0 ± 1 °C.

Analytics and Methods for Logistic System Design and Operations Management

Analytics is the technology working with the manipulation of data to produce information able to change the world we live every day. Analytics have been largely used within the last decade to cluster people’s behaviour to predict their preferences of items to buy, music to listen, movies to watch and even electoral preference. The most advanced companies succeded in controlling people’s behaviour using analytics. Despite the evidence of the super-power of analytics, they are rarely applied to the big data collected within supply chain systems (i.e. distribution network, storage systems and production plants). This PhD thesis explores the fourth research paradigm (i.e. the generation of knowledge from data) applied to supply chain system design and operations management. An ontology defining the entities and the metrics of supply chain systems is used to design data structures for data collection in supply chain systems. The consistency of this data is provided by mathematical demonstrations inspired by the factory physics theory. The availability, quantity and quality of the data within these data structures define different decision patterns. Ten decision patterns are identified, and validated on-field, to address ten different class of design and control problems in the field of supply chain systems research.

The Missing Part of Seed Dispersal Networks: Structure and Robustness of Bat-Fruit Interactions

Mutualistic networks are crucial to the maintenance of ecosystem services. Unfortunately, what we know about seed dispersal networks is based only on bird-fruit interactions. Therefore, we aimed at filling part of this gap by investigating bat-fruit networks. It is known from population studies that: (i) some bat species depend more on fruits than others, and (ii) that some specialized frugivorous bats prefer particular plant genera. We tested whether those preferences affected the structure and robustness of the whole network and the functional roles of species. Nine bat-fruit datasets from the literature were analyzed and all networks showed lower complementary specialization (H(2)' = 0.3760.10, mean 6 SD) and similar nestedness (NODF = 0.5660.12) than pollination networks. All networks were modular (M=0.32 +/- 0.07), and had on average four cohesive subgroups (modules) of tightly connected bats and plants. The composition of those modules followed the genus-genus associations observed at population level (Artibeus-Ficus, Carollia-Piper, and Sturnira-Solanum), although a few of those plant genera were dispersed also by other bats. Bat-fruit networks showed high robustness to simulated cumulative removals of both bats (R = 0.55 +/- 0.10) and plants (R = 0.68 +/- 0.09). Primary frugivores interacted with a larger proportion of the plants available and also occupied more central positions; furthermore, their extinction caused larger changes in network structure. We conclude that bat-fruit networks are highly cohesive and robust mutualistic systems, in which redundancy is high within modules, although modules are complementary to each other. Dietary specialization seems to be an important structuring factor that affects the topology, the guild structure and functional roles in bat-fruit networks.

Deformed Gaussian-orthogonal-ensemble description of small-world networks

The study of spectral behavior of networks has gained enthusiasm over the last few years. In particular, random matrix theory (RMT) concepts have proven to be useful. In discussing transition from regular behavior to fully chaotic behavior it has been found that an extrapolation formula of the Brody type can be used. In the present paper we analyze the regular to chaotic behavior of small world (SW) networks using an extension of the Gaussian orthogonal ensemble. This RMT ensemble, coined the deformed Gaussian orthogonal ensemble (DGOE), supplies a natural foundation of the Brody formula. SW networks follow GOE statistics until a certain range of eigenvalue correlations depending upon the strength of random connections. We show that for these regimes of SW networks where spectral correlations do not follow GOE beyond a certain range, DGOE statistics models the correlations very well. The analysis performed in this paper proves the utility of the DGOE in network physics, as much as it has been useful in other physical systems.

Assessment of groundwater recharge and water fluxes of the Guarani Aquifer System, Brazil

The groundwater recharge and water fluxes of the Guarani Aquifer System in the state of Sao Paulo in Brazil were assessed through a numeric model. The study area (6,748 km(2)) comprises Jacar,-Gua double dagger A(0) and Jacar,-Pepira River watersheds, tributaries of the Tiet River in the central region of the state. GIS based tools were used in the storage, processing and analysis of data. Main hydrologic phenomena were selected, leading to a groundwater conceptual model, taking into account the significant outcrops occurring in the study area. Six recharge zones were related to the geologic formation and structures of the semi-confined and phreatic aquifer. The model was calibrated against the baseflows and static water levels of the wells. The results emphasize the strong interaction of groundwater flows between watersheds and the groundwater inflow into the rivers. It has been concluded that lateral groundwater exchanges between basins, the deep discharges to the regional system, and well exploitation were not significant aquifer outflows when compared to the aquifer recharge. The results have shown that the inflows from the river into the aquifer are significant and have the utmost importance since the aquifer is potentially more vulnerable in these places.