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Abstract: Soil organic carbon decline after land use change from forest to maize usually leads to soil degradation and elevated CO2 emissions. However, limited knowledge is available on the interactions between rates of SOC change and soil erosion and how SOC dynamics vary with soil depth and clay contents. The 13C isotope based CIDE approach (Carbon Input, Decomposition and Erosion) was developed to determine SOC dynamics on erosion prone slopes. The aims of the present study were: (1) to test the applicability of the CIDE approach to determine rates of decomposition and SOC input under particular considerations of concurrent erosion events on three soil types (Alisol, Luvisol, Vertisol), (2) to adapt the CIDE approach to deeper soil layers (10–20 and 20–30 cm) and (3) to determine the variation of decomposition and SOC input with soil depth and soil texture. SOC dynamics were determined for bulk soil and physically separated SOC fractions along three chronosequences after land use change from forest to maize (up to 21 years). Consideration of the effects of soil erosion on SOC dynamics by the CIDE approach yielded a higher total SOC loss (6–32%), a lower decomposition (13–40%) and a lower SOC input (14–31%) relative to the values derived from a commonly applied 13C isotope based mass balance approach. Comparison of decomposition between depth layers revealed that tillage accelerated decomposition in the plough layer (0–10 cm), accounting for 3–34% of total decomposition. With increasing clay contents SOC input and mass of sand sized stable aggregates increased. In addition, decomposition increased with increasing clay contents, too, being attributed to decomposition of labile SOC which was attached to clay particles in the sand sized stable aggregate fraction. In conclusion, this study suggests that in situ SOC dynamics on erosion prone slopes are commonly misrepresented by erosion unadjusted approaches leading to an overestimation of SOC input and underestimation of SOC loss. [Copyright &y& Elsevier]

Introduction: The reliable differentiation of tremor disorders remains a challenge and clinical diagnosis often depends on the subjective interpretation of subtle signs and symptoms. So far electrophysiological tests fail to capture the entirety of information in tremor data. Massive time series feature extraction is a powerful tool to examine oscillating biological signals, such as tremor disorders, in an un-biased way. We previously showed that this method is feasible and can predict the response to non-invasive stimulation (1). This study is aiming to validate and extend our analytical pipeline examining higher-dimensional mathematical features extracted from accelerometer time-signal recordings from tremor patients. This approach will be used to differentiate tremor aetiologies, response to treatment and explore disease mechanisms. Materials and Methods: Accelerometer recordings and corresponding phenotypical information from n=400 patients suffering from essential tremor (ET), dystonic tremor (DT), Parkinsońs disease (PD), functional tremor and monogenic tremor from six different European centres with specialist movement disorder units have been integrated into a singular data set. Sub-cohorts include repeat measurements and measurements under different treatments. Massive higher-order feature extraction is applied to perform supervised and unsupervised statistical learning. We are developing techniques for batch correction to control for centre-specific differences in the data. Results: Raw accelerometer recordings from involved centres were cleaned, aligned, screened for movement artefacts and harmonised. After plausibility checks and quality control, we are establishing the robustness of feature extraction with regards to sampling frequency, applied filters and recording length. Data analysis involves the comparison of different machine learning algorithms for supervised and unsupervised tremor exploration. Conclusions: With this work we aim to explore communalities and differences in tremor syndromes using unbiased and feature-based statistical learning in a clinically and methodologically robust way. To this end, the integration of accelerometer-based movement characteristics from different tremor entities and centres will allow a first objective glimpse at the tremor landscape. This aims to improve clinical diagnostic accuracy and expand our understanding of tremor aetiologies and the underlying mechanisms. References 1. S. R. Schreglmann, D. Wang, R. L. Peach, J. Li, X. Zhang, A. Latorre, E. Rhodes, E. Panella, A. M. Cassara, E. S. Boyden, M. Barahona, S. Santaniello, J. Rothwell, K. P. Bhatia, N. Grossman, Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence. Nat Commun. 12, 363 (2021). [ABSTRACT FROM AUTHOR]