Determination of forest road surface roughness by kinect depth imaging

Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities. Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect\u2122 depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness. The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles

Analysis of the performance of the CHESHIRE and YAPP methods at CASD-NMR round 3.

We present an analysis of the results obtained at CASD-NMR round 3 by the CHESHIRE and the YAPP methods. To determine protein structures, the CHESHIRE method uses solely information provided by NMR chemical shifts, while the YAPP method uses an automated assignment of NOESY spectra. Of the ten targets of CASD-NMR round 3, nine CHESHIRE predictions and eight YAPP ones were submitted. The eight YAPP predictions ranged from 0.7 to 1.9 Å Cα accuracy, with an average of 1.3 Å. The nine CHESHIRE predictions ranged from 0.8 to 2.6 Å Cα accuracy for the ordered regions of the proteins, with an average of 1.6 Å. Taken together, these results illustrate how the NOESY based YAPP method and the chemical shift based CHESHIRE method can provide structures of comparable quality.This is the author accepted manuscript. The final version is available at http://link.springer.com/article/10.1007%2Fs10858-015-9940-9

Using Principal Paths to Walk Through Music and Visual Art Style Spaces Induced by Convolutional Neural Networks

AbstractComputational intelligence, particularly deep learning, offers powerful tools for discriminating and generating samples such as images. Deep learning methods have been used in different artistic contexts for neural style transfer, artistic style recognition, and musical genre recognition. Using a constrained manifold analysis protocol, we discuss to what extent spaces induced by deep-learning convolutional neural networks can capture historical/stylistic progressions in music and visual art. We use a path-finding algorithm, called principal path, to move from one point to another. We apply it to the vector space induced by convolutional neural networks. We perform experiments with visual artworks and songs, considering a subset of classes. Within this simplified scenario, we recover a reasonable historical/stylistic progression in several cases. We use the principal path algorithm to conduct an evolutionary analysis of vector spaces induced by convolutional neural networks. We perform several experiments in the visual art and music spaces. The principal path algorithm finds reasonable connections between visual artworks and songs from different styles/genres with respect to the historical evolution when a subset of classes is considered. This approach could be used in many areas to extract evolutionary information from an arbitrary high-dimensional space and deliver interesting cognitive insights

Spathial: an R package for the evolutionary analysis of biological data

Abstract Summary A primary problem in high-throughput genomics experiments is finding the most important genes involved in biological processes (e.g. tumor progression). In this applications note, we introduce spathial, an R package for navigating high-dimensional data spaces. spathial implements the Principal Path algorithm, which is a topological method for locally navigating on the data manifold. The package, together with the core algorithm, provides several high-level functions for interpreting the results. One of the analyses we propose is the extraction of the genes that are mainly involved in the progress from one state to another. We show a possible application in the context of tumor progression using RNA-Seq and single-cell datasets, and we compare our results with two commonly used algorithms, edgeR and monocle3, respectively. Availability and implementation The R package spathial is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/spathial/index.html) and on GitHub (https://github.com/erikagardini/spathial). It is distributed under the GNU General Public License (version 3). Supplementary information Supplementary data are available at Bioinformatics online

Modelling noise propagation generated by forest operations: a case study in Southern Italy

Noise is defined as an undesired sound that constitutes an unwarranted disturbance potentiality modifying animal behaviour or normal functioning. Forest operations commonly involve the use of equipment and machines that can produce noise and be a potential permanent or temporary disturbance for the wildlife. This study simulates noise propagation in a natural area generated during coppice stand harvesting by direct field noise measurements and the application of a specific GIS model. Two working phases were investigated: felling and yarding operations. Two potential systems were analyzed for the yarding operations: a) yarding by mobile cable yarder and b) skidding by tractor with a three point log grapple. The results are reported in terms of excess noise area for the third-octave bandwidths with the centre in 500 Hz, 1000 Hz and 2000 Hz. Felling by chainsaws presented the largest area where noise exceeds the ambient natural noise, while in the case of yarding, mobile cable yarding operations presented a smaller area of excess noise than skidding by tractor

STAT1 and Its Crucial Role in the Control of Viral Infections

The signal transducer and activator of transcription (STAT) 1 protein plays a key role in the immune response against viruses and other pathogens by transducing, in the nucleus, the signal from type I, type II and type III IFNs. STAT1 activates the transcription of hundreds of genes, some of which have been well characterized for their antiviral properties. STAT1 gene deletion in mice and complete STAT1 deficiency in humans both cause rapid death from severe infections. STAT1 plays a key role in the immunoglobulin class-switch recombination through the upregulation of T-bet; it also plays a key role in the production of T-bet+ memory B cells that contribute to tissue-resident humoral memory by mounting an IgG response during re-infection. Considering the key role of STAT1 in the antiviral immune response, many viruses, including dangerous viruses such as Ebola and SARS-CoV-2, have developed different mechanisms to inhibit this transcription factor. The search for drugs capable of targeting the viral proteins implicated in both viral replication and IFN/STAT1 inhibition is important for the treatment of the most dangerous viral infections and for future viral pandemics, as shown by the clinical results obtained with Paxlovid in patients infected with SARS-CoV-2