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5G New Radio (NR) has stringent demands on both performance and complexity for the design of low-density parity-check (LDPC) decoding algorithms and corresponding VLSI implementations. Furthermore, decoders must fully support the wide range of all 5G NR blocklengths and code rates, which is a significant challenge. In this paper, we present a high-performance and low-complexity LDPC decoder, tailor-made to fulfill the 5G requirements. First, to close the gap between belief propagation (BP) decoding and its approximations in hardware, we propose an extension of adjusted min-sum decoding, called generalized adjusted min-sum (GA-MS) decoding. This decoding algorithm flexibly truncates the incoming messages at the check node level and carefully approximates the non-linear functions of BP decoding to balance the error-rate and hardware complexity. Numerical results demonstrate that the proposed fixed-point GA-MS has only a minor gap of 0.1 dB compared to floating-point BP under various scenarios of 5G standard specifications. Secondly, we present a fully reconfigurable 5G NR LDPC decoder implementation based on GA-MS decoding. Given that memory occupies a substantial portion of the decoder area, we adopt multiple data compression and approximation techniques to reduce 42.2% of the memory overhead. The corresponding 28nm FD-SOI ASIC decoder has a core area of 1.823 mm2 and operates at 895 MHz. It is compatible with all 5G NR LDPC codes and achieves a peak throughput of 24.42 Gbps and a maximum area efficiency of 13.40 Gbps/mm2 at 4 decoding iterations.

Cytochrome P450 family 1 subfamily A member 2 (CYP1A2) performs an indispensable role in the metabolism of both exogenous and endogenous substances. What is more, CYP1A2 functions in human diseases by regulating the homeostasis of cholesterol. Despite the emergence of gene-editing animal models, genetically humanized animals that overcome species differences for further exploring the role of CYP1A2 in drug metabolism and human diseases have not been constructed. In this study, we inserted human CYP1A2cDNA into the rat Cyp1a2gene by using CRISPR/CRISPR-associated protein 9 (Cas9) technology. The results showed that human CYP1A2 was successfully expressed in humanized rat liver, and there were no statistically significant differences of physiologic symptoms compared with wild-type (WT) rats. In vitro incubation results indicated the different inhibition of furafylline on CYP1A2 activity in human liver microsomes, humanized CYP1A2(hCYP1A2) rat liver microsomes, and WT rat liver microsomes, with IC50values of 7.1 μM, 36.5 μM, and 285.8 μM, respectively. Meanwhile, pharmacokinetic characteristics of clozapine were conducted, and the results suggested that in hCYP1A2 rats, clozapine tended to be metabolized into norclozapine. Both the in vitro and in vivo results demonstrated the different metabolic functions of CYP1A2 in humanized and WT rats. We successfully constructed a novel humanized CYP1A2rat model using the CRISPR/Cas9 system, providing a powerful tool for better predicting CYP1A2-mediated drug metabolism and pharmacokinetics.SIGNIFICANCE STATEMENTHuman CYP1A2 takes active part in the biotransformation of both exogenous substances and endogenous substances. Meanwhile, it plays a regulatory role in human diseases, including hypercholesterolemia and hypertension. However, the results obtained from animal models cannot be directly applied to humans. This study successfully constructed a humanized CYP1A2rat model by CRISPR/CRISPR-associated protein 9 technology, providing a powerful model for promoting drug metabolism research as well as exploring the role of CYP1A2 in human diseases.

Compared to the linear MIMO detectors, the Belief Propagation (BP) detector has shown greater capabilities in achieving near-optimal performance and better nature to iteratively cooperate with channel decoders. Aiming at real applications, recent works mainly fall into the category of reducing the complexity by simplified calculations, at the expense of performance sacrifice. However, the complexity is still unsatisfactory with exponentially increasing complexity or required exponentiation operations. Furthermore, the state-of-the-art (SOA) BP detectors persistently encounter error floor in high signal-to-noise ratio (SNR) region, which becomes even worse with calculation approximation. This work aims at a revised BP detector, named Belief-selective Propagation (BsP) detector by selectively utilizing the trusted incoming messages with sufficiently large a priori probabilities for updates. Two proposed strategies: symbol-based truncation (ST) and edge-based simplification (ES) squeeze the complexity (orders lower than the BP detector), while greatly relieving the error floor issue over a wide range of antenna and modulation combinations. For the 256-QAM $128 \times 64$ uplink massive multiuser MIMO (MU-MIMO) system, the $\mathcal {B}(1,1)$ BsP detector achieves more than 1dB performance gain (@ $\text {BER}=10^{-4}$ ) with lower complexity than the state-of-the-art (SOA) BP detector. Trade-off between performance and complexity towards different application requirements can be conveniently obtained by tuning the parameters of the ST and ES strategies.

Polarized-adjusted convolutional (PAC) codes are proposed by Arıkan to improve the performance of polar codes by concatenating a polar transform with a convolutional transform. The error-correction performance of PAC codes under sequential decoding can reach the dispersion approximation bound in certain rate cases. However, due to the serial exploration of the codeword, the time and computational complexity caused by sequential decoding, e.g., Fano decoding, are required to be further reduced. In this article, we propose a low-complexity Fano decoding algorithm for PAC codes, called fast Fano decoding, to reduce the decoding complexity. The decoding algorithm considers four types of special nodes, i.e., constitute codes with special bit distribution patterns, to improve the parallelism degree of inner code decoding by immediately returning the messages from these nodes. Moreover, a node-level rewinding scheme including stage-located and partial memory recovery (SL-PM) is proposed to backtrack the intermediate messages efficiently. The results show that for a PAC code with length 128 and half rate, the proposed fast Fano decoding with the SL-PM rewinding scheme achieves more than 90% time and computational complexity reduction without performance degradation compared to the original Fano decoding.