ALBERT

Description: The acquisition of a stratigraphically intact Antarctic ice column spanning the past 1.5 million years requires understanding of the thermal state of both the basal layer of the ice sheet and the underlying bed, which are influenced strongly by the coupled boundary conditions of geothermal heat flow and accumulation rate. However, geothermal heat flow, crustal structure, lithology, and other geological controls on thermal and hydraulic conductivity are poorly understood for likely ‘old ice’ regions of Antarctica. In the 2022/23 Antarctic field season, we collected over 20,000 line km of new airborne radar, magnetics, and gravity data over a poorly-surveyed region of the East Antarctic Ice Sheet between Dome A and the South Pole. We present updated maps of subglacial topography and ice thickness, as well as free-air and Bouguer anomaly grids, which can be used to make preliminary inferences about the crustal framework and basal thermal regime of the study area. These data, supplemented by existing geophysical observations, will inform further airborne and ground-based geophysical surveys and provide important context for ice flow models and selection of potential sites for old ice drilling operations.

Description: Using Global Navigation Satellite System-Reflectometry (GNSS-R) for soil moisture (SM) retrieval has recently gained importance due to its high temporal-spatial resolution. However, the current methods, i.e., constructing a single machine learning (ML)-based model, have large model uncertainty resulting from ML networks and input schemes. Moreover, traditional Normalized Difference Vegetation Index (NDVI) cannot capture the rapid vegetation changes well. In this paper, a new SM retrieval method of constructing a hybrid model based on Bayesian model averaging (BMA) is employed to reduce the model uncertainty. Meanwhile, novel Sun-induced fluorescence (SIF) data is used as ancillary data to represent the rapid change of vegetation. We validate the proposed method at point and regional scales using in-situ data and the Global Land Data Assimilation System (GLDAS) product. The results demonstrate that our method has high accuracy and low uncertainty in SM retrieval. At the point scale, as accuracy indices, the average R () of BMA increases from 0.90 to 0.93 and the average root-mean-square-error () decreases from 0.034 to 0.029 ; as indices of uncertainty, the standard deviations of R and RMSE ( and ) decrease by 32 % and 9 % compared to the single ML-based model. For the regional scale, the increases from 0.79 to 0.81, the decreases from 0.024 to 0.023 , and the decreases by 19 %. Moreover, we take the point-scale experiment as an example for comparison to compare the performance of SIF with that of NDVI. The of BMA trained by SIF is 0.03 higher than that trained by NDVI and the decreases by 0.002 ; and decrease by 25 % and 6 %. Based on these results, the proposed method can reduce the uncertainty and the advantage of SIF has potential for improving the SM retrieval.

Description: The typical hallmark of electroencephalogram (EEG) in Alzheimer’s disease (AD) is a slowing of rhythms and perturbations in synchrony. However, the mechanism of AD electrophysiological abnormalities is still ambiguous. Synapse deficiency has been considered as an evident neuropathological change in AD that is closely associated with cognitive decline. The main purpose of this work is to explore how synapse deficiency in AD affects these electrophysiological features using neural computational techniques. First, based on the Diffusion Tensor Imaging data, a connectivity matrix of a structural brain network is constructed by means of a pipeline toolbox called PANDA. Using this data-driven connectivity matrix, a cortical network model with 90 cortical areas is then be built in which each cortical area is modeled by a neuron mass model. Subsequently, by reducing the synaptic strength parameter to mimic synapse deficiency in AD, our results show that the synapse deficiency does not only cause a leftward shift of the dominant frequency, but also induces a decrease in the alpha rhythm and an increase in the theta rhythm. Further, the influence of synapse deficiency on phase synchrony is investigated by the phase lag index (PLI). When the synaptic strength parameter is reduced, the alpha-band PLI decreases and theta-band PLI increases. Moreover, a statistical analysis of the differences between the simulated AD and healthy control (HC) in terms of synchronization and rhythms is performed. The results demonstrate that there are significant differences between simulated AD and HC groups. All the above simulation results are consistent with the EEG changes of AD in the physiological experiments. Finally, a strong statistical correlation between PLI and relative power is revealed using Pearson’s correlation analysis. This study reveals a close relationship between synapse deficiency and electrophysiological abnormalities in AD, which may provide new insight for the early diagnosis of AD.