MOESM1 of Both HDAC5 and HDAC6 are required for the proliferation and metastasis of melanoma cells

Additional file 1: Figure S1. HDAC5 and HDAC6 were overexpressed in melanoma cells, and qRT-PCR was used to identify the expression level of all the HDACs except sirtuins in A375 cells, A2058 cells and normal skin cells: HaCat cells. All of HDACs expression levels were firstly normalized to GAPDH and then ratio to HaCaT cells

MOESM2 of Both HDAC5 and HDAC6 are required for the proliferation and metastasis of melanoma cells

Additional file 2: Figure S2. Screening for an efficient shRNA for HDAC5 or HDAC6 knockdown. The seq used for RNA interference are listed in Materials and Methods. HDAC5 (or HADC6) shRNA vectors were transiently transfected in HEK293T cells, and the cells were collected 36 h later, washed twice with ice cold PBS, and centrifuged at 1000 rpm for 5 min. Then, 1 × SDS loading buffer was added and boiled for 10 min; then 10 μl of samples was loaded for SDS-PAGE. β-actin was used as an internal control

MOESM3 of Both HDAC5 and HDAC6 are required for the proliferation and metastasis of melanoma cells

Additional file 3: Figure S3. Knocking down HDAC6 induced apoptosis with time course. Annexin V was used to stain the apoptotic cells and PI was used to stain the dead cells. After constructing knocking down HDAC6 stable cells, we continued to culture these cells in RPMI1640 medium and collected cells with a time course: 1, 3, 5 and 7 days

A Perovskite Memristor with Large Dynamic Space for Analog-Encoded Image Recognition

Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows low-cost hardware implementation. However, the previously reported memristor-based RC mostly utilized binarized data sets to reduce the difficulty of signal processing of the memristor, which inevitably induces data distortion to a certain extent, leading to poor network computing performance. Here, we report on a RC system in a fully memristive architecture based on solution-processed perovskite memristors. The perovskite memristor exhibits 10000 conductance states with a modulation range of more than 4 orders of magnitude. The obtained tens of thousands of finely spaced conductance states with a near-ideal analog property provide a sufficiently large dynamic range and enough intermediate states, which were further applied as a reservoir to map the feature information on different sequential inputs in an analog way. The computing capability of the image classification task of a Fashion-MNIST data set with a high recognition accuracy of up to 90.1% shows that the excellent analog and short-term properties of our perovskite memristor allow the hardware implementation of neuromorphic computing with a reduced training cost