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Additional file 1: Table S1. Pathogenicity prediction of three missense variants and their classification according to the ACMG rules.

Additional file 1: Table S2. Primer sequences from PCR amplification for the exon and breakpoint junctions located in the upstream region of POU3F4.

Additional file 2: Fig. S2. Conservation analysis of variants located in the POU-specific domain and homeodomain of POU3F4. (a) Protein alignment of POU-specific domain across six species. The three residues (R205, V215 and L217) located in the POU-specific domain were highly conserved in these species. (b) Protein alignment of POU homeodomain across six species. The three residues (R282, K302 and V321) located in the POU homeodomain were also highly conserved in these species. (c) Structural simulation of p.Arg205del mutant protein. The 205 residues are marked in green (white arrows) and the difference in protein structures is clearly seen between mutant protein (MT) and wild-type (WT) protein.

Additional file 1: Fig. S1. Motherhood confirmation of three families with de novo variants. 14 genetic markers (D19S433, D5S818, D18S51, D6S1043, D3S1358, D16S539, CSF1PO, VWA, TPOX, Penta E, THO1, D12S391, D2S1338, and FGA) are used to identify paternity. (a) Family 09 with p.Trp57*. The relative chance of paternity (RCP) is 99.9999%. (b) Family 13 with p.Arg205del. The RCP is 99.9999%. (c) Family 14 with g.81807331_81887213del. The RCP is 99.9999%.

Additional file 1: Table S2. Primer sequences from PCR amplification for the exon and breakpoint junctions located in the upstream region of POU3F4.

Additional file 1: Table S1. Pathogenicity prediction of three missense variants and their classification according to the ACMG rules.

Artificial intelligence is widely used in everyday life. However, an insufficient computing efficiency due to the so-called von Neumann bottleneck cannot satisfy the demand for real-time processing of rapidly growing data. Memristive in-memory computing is a promising candidate for highly efficient data processing. However, performance of memristors varies significantly because of microstructure change induced by electric-driven matter migration. Here, we propose an all-optically controlled (AOC) memristor with a simple Au/ZnO/Pt sandwich structure based on a purely electronic tuning mechanism of memconductance. The memconductance can be reversibly tuned only by light irradiation with different wavelengths. The device can be used to perform in-memory computation such as nonvolatile neuromorphic computing and Boolean logic functions. Moreover, no microstructure change is involved during the operation of our AOC memristor which demonstrates superior operation stability. Based on this and its structural simplicity, the device has attractive application prospects for the next generation of computing systems., 44 pages, 19 figures

Memristors have emerged as key candidates for beyond-von-Neumann neuromorphic or in-memory computing owing to the feasibility of their ultrahigh-density three-dimensional integration and their ultralow energy consumption. A memristor is generally a two-terminal electronic element with conductance that varies nonlinearly with external electric stimuli and can be remembered when the electric power is turned off. As an alternative, light can be used to tune the memconductance and endow a memristor with a combination of the advantages of both photonics and electronics. Both increases and decreases in optically induced memconductance have been realized in different memristors; however, the reversible tuning of memconductance with light in the same device remains a considerable challenge that severely restricts the development of optoelectronic memristors. Here we describe an all-optically controlled (AOC) analog memristor with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. Our memristor is based on the relatively mature semiconductor material InGaZnO (IGZO) and a memconductance tuning mechanism of light-induced electron trapping and detrapping. We demonstrate that spike-timing-dependent plasticity (STDP) learning can be realized in our device, indicating its potential applications in AOC spiking neural networks (SNNs) for highly efficient optoelectronic neuromorphic computing., Comment: 28 pages, 12 figures