199,871 research outputs found
Inhibition of Sendai virus hemagglutinin neuraminidase by the fusion protein
No full textThe Sendai virus envelope contains two glycoproteins: the fusion (F) protein and the hemagglutinin-neuraminidase (HN). Inactivation of F causes the loss of fusogenic activity and an increase of the neuraminidase activity of HN. After inactivation of F, HN can be inhibited by fetuin or asialofetuin, as already observed on the water-soluble, C-terminal fragment of HN (Dallocchio, F., Bellini, T., Martuscelli, G., Baiocchi, M., & Tomasi, M. (1991) Biochem. Int. 25, 663-668). Disruption of viral envelopes by detergents does not affect the neuraminidase activity of virions containing inactive F, while it causes an increase of the neuraminidase activity in native virions. Reconstitution of HN into liposomes is accompanied by a decrease of enzymatic activity, due to the random inside-outside distribution of the protein. However, the decrease of the neuraminidase activity is higher in liposomes containing both HN and F. These data suggest that F inhibits the neuraminidase activity of HN
Polycomp : efficient and configurable compression of astronomical timelines
No full textThis paper describes the implementation of polycomp, a open-sourced, publicly available program for compressing one-dimensional data series in tabular format. The program is particularly suited for compressing smooth, noiseless streams of data like pointing information, as one of the algorithms it implements applies a combination of least squares polynomial fitting and discrete Chebyshev transforms that is able to achieve a compression ratio Cr up to ≈40 in the examples discussed in this work. This performance comes at the expense of a loss of information, whose upper bound is configured by the user. I show two areas in which the usage of polycomp is interesting. In the first example, I compress the ephemeris table of an astronomical object (Ganymede), obtaining Cr≈20, with a compression error on the x,y,z coordinates smaller than 1 m. In the second example, I compress the publicly available timelines recorded by the Low Frequency Instrument (LFI), an array of microwave radiometers onboard the ESA Planck spacecraft. The compression reduces the needed storage from ∼6.5TB to 0.75TB (Cr≈9), thus making them small enough to be kept in a portable hard drive
Activation of the Sendai virus fusion protein by receptor binding
No full text2,3 Dehydro-2-deoxy-N-acetyl-neuraminic acid (DNANA) competitively inhibits the neuraminidase activity of Hemagglutinin-neuraminidase (HN) from Sendai virus. The inhibition constant depends on the presence of the Fusion (F) protein, which is 30 microM in the presence of active F protein and 50 microM when the F protein is inactivated. These data correlate with previously reported evidence of interaction of the F protein with HN (Dallocchio, F., Tomasi, M., & Bellini, T. (1994) Biochem. Biophys. Res. Comm. 201, 988-993). Desialyzation of erythrocytes, by Clostridium neuraminidase, lowers the hemolytic activity of SV to < 0.1% of that observed on untreated erythrocytes. However, addition of DNANA causes a concentration-dependent increase of hemolytic activity. Both HN and the F protein are required for the activation of hemolytic activity by DNANA. The affinity constant for DNANA, calculated from the activation of hemolytic activity on desialyzed erythrocytes, is 35 microM, very close to the Ki for neuraminidase activity. These data suggest that the binding of the F protein to HN, induced by the binding to HN of a substrate or a substrate analogue, causes a conformational change which activates the F protein
Ground based Raman lidar for day and night measurements of water vapour in the boundary layer
No full textThe solar-blind Raman-lidar based on a KrF laser (248 nm) developed at Lecce’s University (407 208 N, 187 68 E) is described. The lidar is currently used for day and night measurements of water vapor. The dependence of the measurement range of the lidar on the laser beam divergence is investigated and it is shown that the KrF laser beam divergence can be reduced by a factor A10 by using a quite
simple unstable cavity configuration. The maximum range which was limited to approximately 500 m for a A3 mrad divergence laser beam has increased up to 1200 m with a A0.3 mrad divergence laser beam since the field of view of the telescope was of 1 mrad. Water vapor profiles retrieved from lidar measurements under different operating conditions are presented. The effect of boundary-layer ozone absorption has also been investigated
Convolutional neural networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis
No full textWe describe a novel method for the application of convolutional neural networks (CNNs) to fields defined on the sphere, using the Hierarchical Equal Area Latitude Pixelization scheme (HEALPix). Specifically, we have developed a pixel-based approach to implement convolutional and pooling layers on the spherical surface, similarly to what is commonly done for CNNs applied to Euclidean space. The main advantage of our algorithm is to be fully integrable with existing, highly optimized libraries for NNs (e.g., PyTorch, TensorFlow, etc.). We present two applications of our method: (i) recognition of handwritten digits projected on the sphere; (ii) estimation of cosmological parameter from simulated maps of the cosmic microwave background (CMB). The latter represents the main target of this exploratory work, whose goal is to show the applicability of our CNN to CMB parameter estimation. We have built a simple NN architecture, consisting of four convolutional and pooling layers, and we have used it for all the applications explored herein. Concerning the recognition of handwritten digits, our CNN reaches an accuracy of 95%, comparable with other existing spherical CNNs, and this is true regardless of the position and orientation of the image on the sphere. For CMB-related applications, we tested the CNN on the estimation of a mock cosmological parameter, defining the angular scale at which the power spectrum of a Gaussian field projected on the sphere peaks. We estimated the value of this parameter directly from simulated maps, in several cases: temperature and polarization maps, presence of white noise, and partially covered maps. For temperature maps, the NN performances are comparable with those from standard spectrum-based Bayesian methods. For polarization, CNNs perform about a factor four worse than standard algorithms. Nonetheless, our results demonstrate, for the first time, that CNNs are able to extract information from polarization fields, both in full-sky and masked maps, and to distinguish between E and B-modes in pixel space. Lastly, we have applied our CNN to the estimation of the Thomson scattering optical depth at reionization (τ) from simulated CMB maps. Even without any specific optimization of the NN architecture, we reach an accuracy comparable with standard Bayesian methods. This work represents a first step towards the exploitation of NNs in CMB parameter estimation and demonstrates the feasibility of our approach
Collaborative Research Practices and Shared Infrastructures for Humanities Computing
No full textThe Italian Association for Digital Humanities (AIUCD) was launched in 2011 to promote and disseminate methods and facilitate scientific collaboration and development of useful resources in the field of digital humanities in Italy. AIUCD is an associate organization of the European Association for Digital Humanities (EADH), which brings together and represents the Digital Humanities in Europe across the entire spectrum of disciplines that research, develop, and apply digital humanities methods and technology. AIUCD is thereby represented in the Alliance of Digital Humanities Organizations (ADHO) which promotes and supports digital research and teaching across all arts and humanities disciplines, acting as a community-based advisory force, and supporting world-wide excellence in research, publication, collaboration and training.
We are very pleased to present the volume of the proceedings of the 2nd Annual Conference of the Italian Association for Digital Humanities (AIUCD 2013) on “Collaborative Research Practices and Shared Infrastructures for Humanities Computing”, which took place at the Department of Information Engineering of the University of Padua, 11-12 December 2013
IDENTIFICATION OF MOTOR CONTROL OBJECTIVES IN HUMAN LOCOMOTION VIA MULTI-OBJECTIVE INVERSE OPTIMAL CONTROL
No full textPredictive simulations of human motion are a precious resource for a deeper understanding of the motor control policies encoded by the central nervous system. They also have profound implications for the design and control of assistive and rehabilitation devices, for ergonomics, as well as for surgical planning. However, the potential of state-of-the-art predictive approaches is not fully realized yet, making it difficult to draw convincing conclusions about the actual optimality principles underlying human walking. In the present study we propose a novel formulation of a bilevel, inverse optimal control strategy based on a full-body three-dimensional neuromusculoskeletal model. In the lower level, prediction of walking is formulated as a principled multi-objective optimal control problem based on a weighted Chebyshev metric, whereas the contributions of candidate control objectives are systematically and efficiently identified in the upper level. Our framework has proved to be effective in determining the contributions of the selected objectives and in reproducing salient features of human locomotion. Nonetheless, some deviations from the experimental kinematic and kinetic trajectories have emerged, suggesting directions for future research. The proposed framework can serve as an inverse optimal control platform for testing multiple optimality criteria, with the ultimate goal of learning the control objectives that best explain observed human motion
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