No abstract

No abstract

No abstract

No abstract

When it comes to mechanisms of brain functions such as learning and memory mediated by neural networks, existing multichannel electrophysiological detection and regulation technology at the cellular level does not suffice. To address this challenge, a 128-channel microelectrode array (MEA) was fabricated for electrical stimulation (ES) training and electrophysiological recording of the hippocampal neurons in vitro. The PEDOT:PSS/PtNPs-coated microelectrodes dramatically promote the recording and electrical stimulation performance. The MEA exhibited low impedance (10.94 ± 0.49 kohm), small phase delay (−12.54 ± 0.51°), high charge storage capacity (14.84 ± 2.72 mC/cm2), and high maximum safe injection charge density (4.37 ± 0.22 mC/cm2), meeting the specific requirements for training neural networks in vitro. A series of ESs at various frequencies was applied to the neuronal cultures in vitro, seeking the optimum training mode that enables the neuron to display the most obvious plasticity, and 1 Hz ES was determined. The network learning process, including three consecutive trainings, affected the original random spontaneous activity. Along with that, the firing pattern gradually changed to burst and the correlation and synchrony of the neuronal activity in the network have progressively improved, increasing by 314% and 240%, respectively. The neurons remembered these changes for at least 4 h. Collectively, ES activates the learning and memory functions of neurons, which is manifested in transformations in the discharge pattern and the improvement of network correlation and synchrony. This study offers a high-performance MEA revealing the underlying learning and memory functions of the brain and therefore serves as a useful tool for the development of brain functions in the future.

No abstract

No abstract

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Glacier retreat is evident on all continents, and the rate of retreat has accelerated during recent decades. Accurate, spatially explicit information on the position of glacier margins over time is useful for analyzing patterns of glacier retreat and measuring reductions in glacier surface area. This information is also essential for evaluating how mountain ecosystems are evolving due to climate warming and the attendant glacier retreat. Here, we present a non-comprehensive spatially explicit dataset showing multiple positions of glacier fronts since the Little Ice Age (LIA) maxima, including many data from the pre-satellite era. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings, and aerial or satellite images with a supplement of geochronology; and own field data. We provide ESRI shapefiles showing 728 past positions of 94 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8).