Interference Analysis Between Digital Terrestrial Television (DTT) and 4G LTE Mobile Networks in the Digital Dividend Bands

With the introduction of digital terrestrial television (DTT) and the analogue television switch-off, terrestrial broadcast spectrum in the UHF band is being released for mobile communications, in particular for fourth generation (4G) long term evolution (LTE) mobile services. This spectrum is known as digital dividend. An impending problem when deploying 4G LTE mobile networks in the digital dividend bands is that interferences may appear in the adjacent radio frequency channels used for DTT. In this paper, we analyze the adjacent coexistence of DTT and 4G LTE networks in the digital dividend bands at 700 MHz and 800 MHz. A generic framework is adopted such that results can be easily extrapolated to different scenarios and bands. Results are presented as a function of the guard band between technologies, for both LTE uplink and downlink adjacent to the DTT signals, and for fixed outdoor and portable indoor DTT reception. Also, the effect of using anti-LTE filters is studied.This work was supported by the Spectrum Regulator of Colombia ANE (Agencia Nacional del Espectro).Ribadeneira Ramírez, JA.; Martínez, G.; Gómez Barquero, D.; Cardona, N. (2016). Interference Analysis Between Digital Terrestrial Television (DTT) and 4G LTE Mobile Networks in the Digital Dividend Bands. IEEE Transactions on Broadcasting. 62(1):24-34. doi:10.1109/TBC.2015.2492465S243462

Extreme cycles. The center of a Leavitt path algebra

In this paper we introduce new techniques in order to deepen into the structure of a Leavitt path algebra with the aim of giving a description of the center. Extreme cycles appear for the first time; they concentrate the purely infinite part of a Leavitt path algebra and, jointly with the line points and vertices in cycles without exits, are the key ingredients in order to determine the center of a Leavitt path algebra. Our work will rely on our previous approach to the center of a prime Leavitt path algebra

Fusarium Wilt of Banana: Current Knowledge on Epidemiology and Research Needs Toward Sustainable Disease Management

Banana production is seriously threatened by Fusarium wilt (FW), a disease caused by the soil-borne fungus Fusarium oxysporum f. sp. cubense (Foc). In the mid-twentieth century FW, also known as “Panama disease”, wiped out the Gros Michel banana industry in Central America. The devastation caused by Foc race 1 was mitigated by a shift to resistant Cavendish cultivars, which are currently the source of 99% of banana exports. However, a new strain of Foc, the tropical race 4 (TR4), attacks Cavendish clones and a diverse range of other banana varieties. Foc TR4 has been restricted to East and parts of Southeast Asia for more than 20 years, but since 2010 the disease has spread westward into five additional countries in Southeast and South Asia (Vietnam, Laos, Myanmar, India, and Pakistan) and at the transcontinental level into the Middle East (Oman, Jordan, Lebanon, and Israel) and Africa (Mozambique). The spread of Foc TR4 is of great concern due to the limited knowledge about key aspects of disease epidemiology and the lack of effective management models, including resistant varieties and soil management approaches. In this review we summarize the current knowledge on the epidemiology of FW of banana, highlighting knowledge gaps in pathogen survival and dispersal, factors driving disease intensity, soil and plant microbiome and the dynamics of the disease. Comparisons with FW in other crops were also made to indicate possible differences and commonalities. Our current understanding of the role of main biotic and abiotic factors on disease intensity is reviewed, highlighting research needs and futures directions. Finally, a set of practices and their impact on disease intensity are discussed and proposed as an integrative management approach that could eventually be used by a range of users, including plant protection organizations, researchers, extension workers and growers

The release of wastewater contaminants in the Arctic : a case study from Cambridge Bay, Nunavut, Canada

The treatment of municipal wastewater in the Arctic is challenging due to a variety of financial, operational, climatic and technical issues. To better understand the efficacy of current wastewater treatment in this region and the hazard posed to receiving waters, we assessed the occurrence of contaminants (i.e., pharmaceuticals, antibiotic resistance genes and nutrients) as they moved through a lagoon-based treatment system in Cambridge Bay in Nunavut, Canada. Wastewater treatment in this community is performed by the use of a lagoon-tundra wetland system that is discharged into the marine environment and is representative of current common practices throughout the region. In 2014, samples were collected before and during lagoon discharge from two locations in the main lagoon, one location downstream from the lagoon effluent and three locations offshore. Grab samples were collected to measure nutrients (e.g. total nitrogen and phosphorus) and the presence of antibiotic resistance gene-bearing microbes, and Polar Organic Chemical Integrative Samplers (POCIS) were deployed to collect passively organic contaminants in all locations. A total of six pharmaceuticals were detected from a screen of twenty-eight analytes during the study: atenolol, carbamazepine, clarithromycin, metoprolol, sulfamethoxazole and trimethoprim. The greatest concentrations of nutrients, antibiotic resistance genes (ARGs) and pharmaceuticals were found in sampling locations within the treatment lagoon. Offshore of the release point, we observed limited to no detection of pharmaceuticals and ARGs and no change in total nitrogen and phosphorus from pre-release. We conclude that the current concentrations of monitored pharmaceuticals do not pose a significant hazard at this time to aquatic organisms in Cambridge Bay

Quality estimation of the electrocardiogram using cross-correlation among leads

Background Fast and accurate quality estimation of the electrocardiogram (ECG) signal is a relevant research topic that has attracted considerable interest in the scientific community, particularly due to its impact on tele-medicine monitoring systems, where the ECG is collected by untrained technicians. In recent years, a number of studies have addressed this topic, showing poor performance in discriminating between clinically acceptable and unacceptable ECG records. Methods This paper presents a novel, simple and accurate algorithm to estimate the quality of the 12-lead ECG by exploiting the structure of the cross-covariance matrix among different leads. Ideally, ECG signals from different leads should be highly correlated since they capture the same electrical activation process of the heart. However, in the presence of noise or artifacts the covariance among these signals will be affected. Eigenvalues of the ECG signals covariance matrix are fed into three different supervised binary classifiers. Results and conclusion The performance of these classifiers were evaluated using PhysioNet/CinC Challenge 2011 data. Our best quality classifier achieved an accuracy of 0.898 in the test set, while having a complexity well below the results of contestants who participated in the Challenge, thus making it suitable for implementation in current cellular devices.National Institute of General Medical Sciences (U.S.) (Grant R01GM104987)Spain (Research Grant TEC2013-46067-R)Spain (Research Grant TEC2013-48439-C4-1-R)Spain (Research Grant TEC2010-19263

What lies behind the curtain: Cryptic diversity in helminth parasites of human and veterinary importance

Parasite cryptic species are morphologically indistinguishable but genetically distinct organisms, leading to taxa with unclear species boundaries. Speciation mechanisms such as cospeciation, host colonization, taxon pulse, and oscillation may lead to the emergence of cryptic species, influencing host-parasite interactions, parasite ecology, distribution, and biodiversity. The study of cryptic species diversity in helminth parasites of human and veterinary importance has gained relevance, since their distribution may affect clinical and epidemiological features such as pathogenicity, virulence, drug resistance and susceptibility, mortality, and morbidity, ultimately affecting patient management, course, and outcome of treatment. At the same time, the need for recognition of cryptic species diversity has implied a transition from morphological to molecular diagnostic methods, which are becoming more available and accessible in parasitology. Here, we discuss the general approaches for cryptic species delineation and summarize some examples found in nematodes, trematodes and cestodes of medical and veterinary importance, along with the clinical implications of their taxonomic status. Lastly, we highlight the need for the correct interpretation of molecular information, and the correct use of definitions when reporting or describing new cryptic species in parasitology, since molecular and morphological data should be integrated whenever possibleUniversidad de Costa Rica/[430-B7-733]/UCR/Costa RicaUCR::Vicerrectoría de Docencia::Salud::Facultad de MicrobiologíaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET

Serological and molecular evidence of hepatitis B vírus infection in swine from Brazil.

Projeto/Plano de Ação: 02.11.01.006

RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis.

In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts. Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet's performance was quantitatively evaluated against experts' evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal). The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps < 0.0001). The proposed multimodal RimNet prototype clearly outperformed the best unimodal approach (AUC = 0.943, P < 0.0001). The sensitivity and specificity achieved by RimNet (70.6% and 94.9%, respectively) are comparable to those of experts at the lesion level. In the patient-wise analysis, RimNet performed with an accuracy of 89.5% and a Dice coefficient (or F1 score) of 83.5%. The proposed prototype showed promising performance, supporting the usage of RimNet for speeding up and standardizing the paramagnetic rim lesions analysis in MS

Anemos : development of a next generation wind power forecasting system for the large-scale integration of onshore & offshore wind farms

International audienceThis paper presents the objectives and the research work carried out in the frame of the ANEMOS project on short-term wind power forecasting. The aim of the project is to develop accurate models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting, exploiting both statistical and physical modeling approaches. The project focus on prediction horizons up to 48 hours ahead and investigates predictability of wind for higher horizons up to 7 days ahead useful i.e. for maintenance scheduling. Emphasis is given on the integration of highresolution meteorological forecasts. For the offshore case, marine meteorology is considered as well as information by satellite-radar images. An integrated software platform, ‘ANEMOS', is developed to host the various models. This system will be installed by several utilities for on-line operation at onshore and offshore wind farms for prediction at a local, regional and national scale. The applications include different terrain types and wind climates, on- and offshore cases, and interconnected or island grids. The on-line operation by the utilities will allow validation of the models and an analysis of the value of wind prediction for a competitive integration of wind energy in the developing liberalized electricity markets in the EU