1,720,986 research outputs found
Very late relapse in an HCV genotype 3-infected patient treated with direct-acting antivirals (DAA)
No full textMediterranean Extensive Green Roof Self-Sustainability Mediated by Substrate Composition and Plant Strategy
Full text linkIn the cultivation of extensive green roofs (EGRs), substrate composition is a key aspect together with the evaluation of suitable recycled materials. Recycling materials as amendments can improve the establishment of a self-sustainable EGR, thus providing ecosystem services and benefits from a circular economy and climate change perspective. This study investigates the effects of compost and paper sludge on water retention, substrate temperature attenuation and plant diversity in an EGR experiment. The substrates were composed of tephra (V), compost (C) and paper sludge (P) as follows: VC, as control, VPC and VP. Herbaceous species with different ecological functionality (succulents, annuals, perennials, legumes, geophytes) were sown and/or transplanted with no cultivation inputs. Plant community composition -abundance- and diversity-richness-, substrate water retention and temperature were analyzed. The VPC and VC had the same average substrate temperature, with values lower than VP. The water retention capacity was higher in VC, thanks to the presence of compost. The substrate with paper sludge (VPC and VP) showed the highest species diversity. The VPC substrate was the best compromise for EGR temperature mitigation and plant diversity improvement. Plant functional types in EGRs can be increased, and thus the biodiversity, by modulating the quality and percentage of amendments. The substrate composition can also affect water retention and substrate temperature. In addition, the use of recycling paper sludge in growing media is a winning strategy to reduce waste
Sensory fusion and emerging behaviours in an anthropomorphic robot as a man-machine interface
No full textScientists involved in developing human-machine interfaces have recently begun to address complex problems such as biological believability and how to design machines which can
perceive, learn and make choices. Once this ambitious target has been reached, dynamic human-machine interactions will have to be studied focusing in particular on possible changes in the interactive structure and thus on any manifestations of emerging behaviours.
The FACE (Facial Automaton for Conveying Emotions) project
addresses both issues. FACE is a life-like artifact intended as a
believable human-machine interface that is able to engage in nonverbal communication by imitating and learning the emotional behaviour of an interlocutor. This paper outlines the biomimetic systems it is equipped with, the main focus being on neural control architecture which consists of a sensory-motor map that can permit sensory fusion plus a neurocontroller able to navigate within a simplified behavioural space. The neurocontroller is based on recent discoveries of the role of astrocytes in cognitive processes
Hepatitis C Genotype 4 Virus Nonstructural 3 and Nonstructural 5A Resistance-associated Substitutions in a 16-year-old Adolescent Failing Ombitasvir/Paritaprevir/Ritonavir Plus Ribavirin
No full textPreexistence and appearance of resistance-associated substitutions limit the efficacy of direct-acting antivirals in treatment of hepatitis C. This is the first case report of an adolescent with chronic hepatitis C virus genotype 4 infection and cirrhosis who failed treatment with ombitasvir/paritaprevir/ritonavir and ribavirin. Resistance analysis showed baseline resistance-associated substitutions M28V and Y93C and emergent D168H
Longitudinal study in HIV/HCV-coinfected HAART-naive patients and role of HCV genotype
No full textTo evaluate the impact of highly active antiretroviral therapy (HAART) on the course of hepatitis C (HCV) infection, we studied the biological and virological characteristics of 23 HCV/HIV-coinfected HAART-naive patients. The HCV genotype, HCV and HIV viral loads, serum alanine aminotransferase, CD4+ and CD8+ cell/mm3 were determined at baseline, 1 month, 6 months and 12 months after initiation of HAART. Results were analyzed both in terms of total population and of HCV genotype. The study of the total population suggests that this therapy did not determine a significant alteration of HCV viremia and levels of ALT, while a significant decrease in HIV viremia (-1.7log10 at one year from the start of HAART) and increase in CD4+ counts was observed (P < 0.005). The biological and virological parameters of HCV/HIV coinfection differed according to the HCV genotype. In particular, only genotype 4 showed a significant inverse correlation between HCV and HIV viral loads
Raman spectroscopy and topological machine learning for cancer grading
No full textIn the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system
A multifunctional alternative lawn where warm-season grass and cold-season flowers coexist
No full textLawns provide green infrastructure and ecosystem services for anthropized areas. They have a strong impact on the environment in terms of inputs (water and fertilizers) and maintenance. The use of warm-season grasses, such as Cynodon dactylon (L.) Pers., provides a cost-effective and sustainable lawn in the dry summers of the Mediterranean. In winter, Bermudagrass is dormant and brown, which instead of being a problem could be an opportunity for biodiversity through the coexistence of flowering species. This study assesses the possibility of growing autumn-to-spring-flowering bulbs and forbs with Bermudagrass, to provide ecosystem services in urban areas. Eight geophytes and 18 forbs were incorporated into a mature turf of hybrid Bermudagrass, Cynodon dactylon × C. transvaalensis cv. “Tifway”. At the same time, a commercial flowering mix was sown in the same conditions. Two different soil preparations, scalping and turf flaming, and two different nitrogen doses, 50 and 150 kg ha−1, were carried out before sowing and transplanting. The flowering plants were counted. All the bulbs and six of the 18 forbs were able to grow and flower in the first and second years. The commercial mix was in full bloom from April until the cutting time for the hybrid Bermudagrass, at the end of May. Adding the flowering species did not affect the healthy growth of the warm-season grass. The fertilization dose had no effect, while turf flaming led to a wider spread of Bellis perennis L. and Crocus spp. Several flower-visiting insects were observed in the spring
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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