Infratentorial lesions have been assigned an equivalent weighting to supratentorial plaques in the new McDonald criteria for diagnosing multiple sclerosis. Moreover, their presence has been shown to have prognostic value for disability. However, their spatial distribution and impact on network damage is not well understood. As a preliminary step in this study, we mapped the overall infratentorial lesion pattern in relapsing–remitting multiple sclerosis patients (N = 317) using MRI, finding the pons (lesion density, 14.25/cm3) and peduncles (13.38/cm3) to be predilection sites for infratentorial lesions. Based on these results, 118 fiber bundles from 15 healthy controls and a subgroup of 23 patients showing lesions unilaterally at the predilection sites were compared using diffusion tensor imaging to analyze the impact of an isolated infratentorial lesion on the affected fiber tracts. Fractional anisotropy, mean diffusion as well as axial and radial diffusivity were investigated at the lesion site and along the entire fiber tract. Infratentorial lesions were found to have an impact on the fractional anisotropy and radial diffusivity not only at the lesion site itself but also along the entire affected fiber tract. As previously found in animal experiments, inflammatory attack in the posterior fossa in multiple sclerosis impacts the whole affected fiber tract. Here, this damaging effect, reflected by changes in diffusivity measures, was detected in vivo in multiple sclerosis patients in early stages of the disease, thus demonstrating the influence of a focal immune attack on more distant networks, and emphasizing the pathophysiological role of Wallerian degeneration in multiple sclerosis.
We present a simple introduction to the decision tree algorithm using some examples from nuclear physics. We show how to improve the accuracy of the classical liquid drop nuclear mass model by performing feature engineering with a decision tree. Finally, we apply the method to the Duflo–Zuker model showing that, despite their simplicity, decision trees are capable of improving the description of nuclear masses using a limited number of free parameters.
We present three different methods to estimate error bars on the predictions made using a neural network (NN). All of them represent lower bounds for the extrapolation errors. At first, we illustrate the methods through a simple toy model, then, we apply them to some realistic case related to nuclear masses. By using theoretical data simulated either with a liquid-drop model or a Skyrme energy density functional, we benchmark the extrapolation performance of the NN in regions of the Segrè chart far away from the ones used for the training and validation. Finally, we discuss how error bars can help identifying when the extrapolation becomes too uncertain and thus not reliable.
<p>Diurnal geomagnetic variations are generated in the magnetosphere and last for about 24 hours. These can be seen on the recordings of all magnetic observatories, with amplitudes of several tens of nT, on all magnetic components. The shape and amplitude of diurnal variations strongly depend on the geographical latitude of the observatory. In addition to the dominant external source from the interaction with the magnetosphere, the diurnal geomagnetic variation is also influenced by local phenomena, mainly due to internal electric fields. External influence remains unchanged over distances of hundreds of kilometers, while internal influence may differ over very short distances due to the underground conductivity. The ration of the diurnal geomagnetic variation at two stations should be stable in calm periods and could be destroyed by the phenomena that can occur during the preparation of an earthquake, when at the station inside the seismogenic zone, the underground conductivity would change or additional currents would appear. The cracking process inside the lithosphere before and during earthquakes occurrence, possibly modifies the under- ground electrical structure and emits electro-magnetic waves.</p><p>In this paper, we study how the diurnal geomagnetic field variations are related to Mw>4.9 earthquakes occurred in Vrancea, Romania. For this purpose, we use two magnetometers situated at 150 km away from each other, one, the Muntele Rosu (MLR) observatory of NIEP, inside the Vrancea seismic zone and the other, the Surlari (SUA) observatory of IGR and INERMAGNET, outside the preparation area of moderate earthquakes. We have studied the daily ranges of the magnetic diurnal variation, R=DBMLR/DBSUA, during the last 10 years, to identify behavior patterns associated with external or internal conditions, where DB= Bmax-Bmin, during a 24 hours period.</p><p>As a first conclusion, we can mention the fact that the only visible disturbances appear before some earthquakes in Vrancea with Mw> 5.5, when we see a differentiation of the two recordings due to possible local internal phenomena at MLR. The differentiation consists in the decrease of the value of the vertical component Bzmax-Bzmin at MLR compared to the USA a few days before the earthquake and the return to the initial value after the earthquake. These studies need to be continued in order to determine if it is a repetitive behavior, or if it is just an isolated phenomenon.</p><p><strong>Acknowledgments:</strong></p><p>The research was supported by: the NUCLEU program (MULTIRISC) of the Romanian Ministry of Research and Innovation through the projects PN19080102 and by the Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) through the projects PN-III-P2-2.1-PED-2019-1693, 480 PED/2020 (PHENOMENAL) and PN-III-P4-ID-PCE- 2020-1361, 119 PCE/2021 (AFROS).</p>
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