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Embodied cognition research on Parkinson's disease (PD) points to disruptions of frontostriatal language functions as sensitive targets for clinical assessment. However, no existing approach has been tested for crosslinguistic validity, let alone by combining naturalistic tasks with machine-learning tools. To address these issues, we conducted the first classifier-based examination of morphological processing (a core frontostriatal function) in spontaneous monologues from PD patients across three typologically different languages. The study comprised 330 participants, encompassing speakers of Spanish (61 patients, 57 matched controls), German (88 patients, 88 matched controls), and Czech (20 patients, 16 matched controls). All subjects described the activities they perform during a regular day, and their monologues were automatically coded via morphological tagging, a computerized method that labels each word with a part-of-speech tag (e.g., noun, verb) and specific morphological tags (e.g., person, gender, number, tense). The ensuing data were subjected to machine-learning analyses to assess whether differential morphological patterns could classify between patients and controls and reflect the former's degree of motor impairment. Results showed robust classification rates, with over 80% of patients being discriminated from controls in each language separately. Moreover, the most discriminative morphological features were associated with the patients' motor compromise (as indicated by Pearson r correlations between predicted and collected motor impairment scores that ranged from moderate to moderate-to-strong across languages). Taken together, our results suggest that morphological patterning, an embodied frontostriatal domain, may be distinctively affected in PD across languages and even under ecological testing conditions.

In this work, we investigate various types of ionospheric disturbances observed over Europe following the earthquake that occurred in Turkey on 6 February 2023. By combining observations from Doppler sounding systems, ionosondes, and GNSS receivers, we are able to discern different types of disturbances, propagating with different velocities and through different mechanisms. We can detect co‐seismic ionospheric disturbances close to the epicenter, as well as ionospheric signatures of acoustic waves propagating as a consequence of propagating seismic waves. Unlike the vast majority of past ionospheric co‐seismic disturbance studies that are primarily based on Total Electron Content variations, reflecting disturbances propagating around the F‐region peak, the focus of the present study is the manifestation of disturbances at different ionospheric altitudes by exploiting complementary ionospheric remote sensing techniques. This is particularly highlighted through ionospheric earthquake‐related signatures established as specific ionogram deformations known as multiple‐cusp signatures which appear as additional cusps at the base of the F‐region attributed to electron density irregularities generated by Rayleigh surface waves that generate acoustic waves propagating up to the ionosphere. Therefore this study underlines the advantage that multi‐instrument investigations offer in identifying the propagation of earthquake‐related ionospheric disturbances at different ionospheric altitudes and distances from the earthquake epicenter. The 2023 Turkey earthquake induced a spectrum of ionospheric disturbances as shown by networks of GNSS receivers, ionosondes and HF DopplerDisturbances appeared as shock acoustic wave‐induced traveling ionospheric disturbances in the near field and Rayleigh wave‐induced ionogram multiple‐cusp signature at longer distancesMulti‐instrument remote sensing techniques can detect ionospheric disturbances at a range of altitudes‐distances from the epicenter The 2023 Turkey earthquake induced a spectrum of ionospheric disturbances as shown by networks of GNSS receivers, ionosondes and HF Doppler Disturbances appeared as shock acoustic wave‐induced traveling ionospheric disturbances in the near field and Rayleigh wave‐induced ionogram multiple‐cusp signature at longer distances Multi‐instrument remote sensing techniques can detect ionospheric disturbances at a range of altitudes‐distances from the epicenter

Motor speech disorders in multiple sclerosis (MS) are poorly understood and their quantitative, objective acoustic characterization remains limited. Additionally, little data regarding relationships between the severity of speech disorders and neurological involvement in MS, as well as the contribution of pyramidal and cerebellar functional systems on speech phenotypes, is available.

Although articulatory deficits represent an important manifestation of dysarthria in Parkinson’s disease (PD), the most widely used methods currently available for the automatic evaluation of speech performance are focused on the assessment of dysphonia. The aim of the present study was to design a reliable automatic approach for the precise estimation of articulatory deficits in PD. Twenty-four individuals diagnosed with de novo PD and twenty-two age-matched healthy controls were recruited. Each participant performed diadochokinetic tasks based upon the fast repetition of /pa/-/ta/-/ka/ syllables. All phonemes were manually labeled and an algorithm for their automatic detection was designed. Subsequently, 13 features describing six different articulatory aspects of speech including vowel quality, coordination of laryngeal and supralaryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing were analyzed. In addition, a classification experiment using a support vector machine based on articulatory features was proposed to differentiate between PD patients and healthy controls. The proposed detection algorithm reached approximately 80% accuracy for a 5 ms threshold of absolute difference between manually labeled references and automatically detected positions. When compared to controls, PD patients showed impaired articulatory performance in all investigated speech dimensions ( $p < 0.05$). Moreover, using the six features representing different aspects of articulation, the best overall classification result attained a success rate of 88% in separating PD from controls. Imprecise consonant articulation was found to be the most powerful indicator of PD-related dysarthria. We envisage our approach as the first step towards development of acoustic methods allowing the automated assessment of articulatory features in dysarthrias.

Propagation of medium-scale gravity waves (GWs) in the thermosphere/ionosphere is observed remotely, using multi-frequency and multi-point continuous Doppler sounding system located in the western part of Czechia. Reflection heights of the sounding radio waves are determined from a nearby ionosonde. Phase velocity vectors of GWs are calculated from time/phase delays between signals corresponding to different transmitter–receiver pairs that reflect in the ionosphere at different locations. As various frequencies reflect at different heights, reflection points of radio signals are separated both horizontally and vertically, and the investigation of GW propagation in the ionosphere is performed in three dimensions. Results obtained for two 1-year periods representing the solar maximum (July 2014–June 2015) and current solar minimum (September 2018–August 2019) are presented. It is shown that GWs in the ionosphere usually propagated with wave vectors directed obliquely downward. A statistical distribution of wave vector elevation angles is presented. A model of neutral winds is used to estimate the wave characteristics in the wind-rest frame. It is found that the distribution of elevation angles is narrower in the wind-rest frame than in the Earth frame. Seasonal and diurnal changes of propagation directions and attenuations of GWs are discussed. The wind-rest frame wavelengths of the analyzed GWs were usually from ~ 80 to 300 km, and the propagation velocities were mostly between ~ 100 and ~ 220 m/s.

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