High-performance ferroelectric field-effect transistors with ultra-thin indium tin oxide channels for flexible and transparent electronics.

With the development of wearable devices and hafnium-based ferroelectrics (FE), there is an increasing demand for high-performance flexible ferroelectric memories. However, developing ferroelectric memories that simultaneously exhibit good flexibility and significant performance has proven challenging. Here, we developed a high-performance flexible field-effect transistor (FeFET) device with a thermal budget of less than 400 °C by integrating Zr-doped HfO2 (HZO) and ultra-thin indium tin oxide (ITO). The proposed FeFET has a large memory window (MW) of 2.78 V, a high current on/off ratio (ION/IOFF) of over 108, and high endurance up to 2×107 cycles. In addition, the FeFETs under different bending conditions exhibit excellent neuromorphic properties. The device exhibits excellent bending reliability over 5×105 pulse cycles at a bending radius of 5 mm. The efficient integration of hafnium-based ferroelectric materials with promising ultrathin channel materials (ITO) offers unique opportunities to enable high-performance back-end-of-line (BEOL) compatible wearable FeFETs for edge intelligence applications.

Coherent optical transmitter IQ imbalance mitigation with embedded carrier phase and frequency offset estimation.

We present a 4×4 real-valued channel equalizer with embedded phase estimator, designed for carrier phase and frequency offset estimation and compensation in coherent optical communications with in-phase/quadrature (IQ) impairments. These impairments include IQ timing skew, gain imbalance, and quadrature phase errors at the transmitter side. To achieve adaptive control of the equalizer's filter coefficients, we employ the decision-directed least mean square (DD-LMS) algorithm. This algorithm minimizes the error between the filter outputs and the desired signals in a symbol-by-symbol manner, resulting in faster channel coefficients adaptation speed. Simulation results for a 60 GBaud polarization-multiplexing 16 quadrature amplitude modulation (PM-16QAM) signal demonstrate that our proposed equalizer outperforms a 2×2 cascaded multi modulus algorithm-based (CMMA-based) equalizer, a 2×2 complex-valued method based on the DD-LMS algorithm, and the 4×4 real-valued equalizer without embedded frequency offset estimation (FOE), when the transmitter IQ impairments exist. Experimental validation is also conducted for the 60 GBaud PM-16QAM and 45 GBaud PM-64QAM signals. Similar to the simulation results, our experiments show that the proposed 4×4 real-valued equalizer with embedded FOE can achieve effective SNR penalties of less than 0.41 dB at 7 ps skew, 1.33 dB at 3.5 dB gain imbalance, and 1.64 dB at the bias voltage shift of 0.5 V for the 60 GBaud PM-16QAM signal. Finally, our experimental results confirm the effectiveness of our proposed method in carrier phase estimation as well as the frequency offset compensation, when compared with 4×4 real-valued equalizer without embedded FOE.

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry.

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.

Human bone marrow-derived mesenchymal stem overexpressing microRNA-124-3p inhibit DLBCL progression by downregulating the NFATc1/cMYC pathway.

BACKGROUND: Exosomes play important roles in intercellular communication by delivering microRNAs (miRNAs) that mediate tumor initiation and development, including those in diffuse large B cell lymphoma (DLBCL). To date, however, limited studies on the inhibitory effect of exosomes derived from human bone marrow mesenchymal stem cells (hBMSCs) on DLBCL progression have been reported. Therefore, this study aimed to investigate the role of hBMSC exosomes carrying microRNA-124-3p in the development of DLBCL. METHODS: Microarray-based expression analysis was adopted to identify differentially expressed genes and regulatory miRNAs, which revealed the candidate NFATc1. Next, the binding affinity between miR-124-3p and NFATc1 was detected by luciferase activity assays. The mechanism underlying NFATc1 regulation was investigated using lentiviral transfections. Subsequently, DLBCL cells were cocultured with exosomes derived from hBMSCs transfected with a miR-124-3p mimic or control. Proliferation and apoptosis were measured in vitro. Finally, the effects of hBMSC-miR-124-3p on tumor growth were investigated in vivo. RESULTS: MiR-124-3p was expressed at low levels, while NFATc1 was highly expressed in DLBCL cells. MiR-124-3p specifically targeted and negatively regulated the expression of NFATc1 in DLBCL cells, upregulated miR-124-3p-inhibited DLBCL cell proliferation and promoted apoptosis. The miR-124-3p derived from hBMSCs inhibits tumor growth both in vivo and in vitro via downregulation of the NFATc1/cMYC pathway. CONCLUSION: Human bone marrow-derived mesenchymal stem cell overexpressing microRNA-124-3p represses the development of DLBCL through the downregulation of NFATc1.

Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing.

In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude higher than those of biological synapses (∼10 fJ per spike). Herein, a new type of wearable organic ferroelectric artificial synapse is proposed, which has two modulation modes (optical and electrical modulation). Because of the high photosensitivity of organic semiconductors and the ultrafast polarization switching of ferroelectric materials, the synaptic device has an ultrafast operation speed of 30 ns and an ultralow power consumption of 0.0675 aJ per synaptic event. Under combined photoelectric modulation, the artificial synapse realizes associative learning. The proposed artificial synapse with ultralow power consumption demonstrates good synaptic plasticity under different bending strains. This provides new avenues for the construction of ultralow power artificial intelligence system and the development of future wearable devices.