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The conventional approach to image recognition has been based on raster graphics, which can suffer from aliasing and information loss when scaled up or down. In this paper, we propose a novel approach that leverages the benefits of vector graphics for object localization and classification. Our method, called YOLaT (You Only Look at Text), takes the textual document of vector graphics as input, rather than rendering it into pixels. YOLaT builds multi-graphs to model the structural and spatial information in vector graphics and utilizes a dual-stream graph neural network (GNN) to detect objects from the graph. However, for real-world vector graphics, YOLaT only models in flat GNN with vertexes as nodes ignore higher-level information of vector data. Therefore, we propose YOLaT++ to learn Multi-level Abstraction Feature Learning from a new perspective: Primitive Shapes to Curves and Points. On the other hand, given few public datasets focus on vector graphics, data-driven learning cannot exert its full power on this format. We provide a large-scale and challenging dataset for Chart-based Vector Graphics Detection and Chart Understanding, termed VG-DCU, with vector graphics, raster graphics, annotations, and raw data drawn for creating these vector charts. Experiments show that the YOLaT series outperforms both vector graphics and raster graphics-based object detection methods on both subsets of VG-DCU in terms of both accuracy and efficiency, showcasing the potential of vector graphics for image recognition tasks.

The evolution of wireless networks gravitates toward connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world. Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence by delivering high-quality, low-latency, and privacy-preserving AI services at the network edge. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements, leveraging the power of large language models (LLMs), that is, generative pretrained transformer (GPT). By exploiting the powerful abilities of GPT in language understanding, planning, and code generation, as well as incorporating classic wisdom such as task-oriented communication and edge federated learning, we present a versatile framework that efficiently coordinates edge AI models to cater to users' personal demands while automatically generating code to train new models in a privacy-preserving manner. Experimental results demonstrate the system's remarkable ability to accurately comprehend user demands, efficiently execute AI models with minimal cost, and effectively create high-performance AI models at edge servers.

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.

In this letter, we present a novel approach to improve belief propagation (BP) decoding performance by sparsifying the Tanner graph. Our proposed method first constructs a low-weight parity-check (LWPC) matrix with increased rows based on the original parity-check matrix. Then, all 4-cycles in the LWPC matrix are eliminated by adding auxiliary variable nodes and corresponding parity-check rows, resulting in the generalized LWPC (G-LWPC) matrix. Compared to the original matrix, the sparsity is greatly improved without altering the encoding constraints. Simulation results show that BP decoding based on the G-LWPC matrix is particularly effective for short code lengths. For a (128, 20) polar code with 24-bit cyclic redundancy check, our proposed G-LWPC matrix reduces the density from 17.4% to 1.3%, and with 10 iterations, BP decoding can achieve the performance of successive cancellation list decoding with a list size of 32.

Due to its high parallelism, belief propagation (BP) decoding is amenable to high-throughput applications and thus represents a promising solution for the ultra-high peak data rate required by future communication systems. To bridge the performance gap compared to the widely used successive cancellation list (SCL) decoding algorithm, BP list (BPL) decoding for polar codes extends candidate codeword exploration via multiple permuted factor graphs (PFGs) to improve the error-correcting performance of BP decoding. However, it is a significant challenge to design a unified and flexible BPL hardware architecture that supports various PFGs and code configurations. In this paper, we present the first VLSI implementation of a BPL decoder for polar codes that overcomes this implementation challenge with a hardware-friendly algorithm for on-the-fly flexible permutations. First, we introduce a sequential generation (SG) algorithm to obtain a near-optimal PFG set. Additionally, we demonstrate that any permutation can be decomposed into a combination of multiple fixed routings, and design a low-complexity permutation network to generate graphs in an on-the-fly fashion. Our BPL decoder has a low decoding latency by executing decoding and permutation generation in parallel and supports arbitrary list sizes without area overhead. Experimental results based on $28$nm FD-SOI technology show that for length-$\boldsymbol{1024}$ polar codes with a code rate of one-half, our BPL decoder with $\boldsymbol{32}$ PFGs exhibits similar error-correcting performance to SCL with a list size of $4$ and achieves an average throughput of $\boldsymbol{25.63}$ Gbps and an area efficiency of $\boldsymbol{29.46}$ Gbps/mm$\boldsymbol{{}^{2}}$, which is $\boldsymbol{1.82\times}$ and $\boldsymbol{4.33\times}$ faster than the state-of-the-art BP flip and SCL decoders, respectively.

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.

The increasing integration of wind power in power systems necessitates the probabilistic assessment of various uncertain factors. In operational planning, modeling short-term scale uncertainties, i.e., wind power forecast errors, plays an important role. In this paper, according to the different forecast values, the corresponding probability distributions of wind power forecast errors are developed using a data-driven manner. Then, the polynomial chaos expansion surrogate is developed to facilitate the probabilistic load margin assessment considering wind power forecast errors. The effectiveness of the forecast error model is verified using the historical data of realistic wind power plants. The results show that the probability distributions of forecast errors vary with the level of forecast values. Moreover, the performance of the polynomial chaos expansion surrogate in estimating probabilistic load margin is validated in the IEEE 30-bus system. The results demonstrate that the versatile forecast error distributions significantly impact the characteristics of load margin. Moreover, the polynomial chaos expansion surrogate can accelerate the load margin assessment compared to the Monter Carlo simulation while retaining the same accuracy.

Probabilistic power flow is one of the fundamental tools for assessing the impacts of uncertainties on the operating states of power systems. However, this analysis requires sufficient historical data to obtain precise probability distributions of input variables, which may not be met in practical engineering problems. In this paper, input variables with insufficient data are represented by parametric probability boxes (p-boxes), i.e., probability distributions with imprecise parameters. In order to facilitate the uncertain power flow calculation with p-boxes, a polynomial chaos expansion-based method is developed. Moreover, the interval-valued Borgonovo index is proposed for global sensitivity analysis and to identify the input variables that have critical impacts on systems. The simulations in IEEE 14-bus and 118-bus systems verify the accuracy and efficiency of the proposed method by comparing it with the conventional double-loop sampling method.

Terahertz ultra-massive MIMO (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless networks, for which channel estimation is highly challenging. Traditional analytical estimation methods are no longer effective, as the enlarged array aperture and the small wavelength result in a mixture of far-field and near-field paths, constituting a hybrid-field channel. Deep learning (DL)-based methods, despite the competitive performance, generally lack theoretical guarantees and scale poorly with the size of the array. In this article, we propose a general DL framework for THz UM-MIMO channel estimation, which leverages existing iterative channel estimators and is with provable guarantees. Each iteration is implemented by a fixed point network (FPN), consisting of a closed-form linear estimator and a DL-based non-linear estimator. The proposed method perfectly matches the THz UM-MIMO channel estimation due to several unique advantages. First, the complexity is low and adaptive. It enjoys provable linear convergence with a low per-iteration cost and monotonically increasing accuracy, which enables an adaptive accuracy-complexity tradeoff. Second, it is robust to practical distribution shifts and can directly generalize to a variety of heavily out-of-distribution scenarios with almost no performance loss, which is suitable for the complicated THz channel conditions. For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect. Theoretical analysis and extensive simulation results are provided to illustrate the advantages over the state-of-the-art methods in estimation accuracy, convergence rate, complexity, and robustness.

Channel coding and multiple-input multiple-output (MIMO) antennas are two key technologies in the modern communication system. For MIMO detection, the expectation propagation (EP) detector has been proved to outperform classical and advanced solutions. Merging with the parity constraints of channel coding, a novel EP detection with linear programming (LP), named EPLP, is proposed to change the common initialization that the symbols are equiprobable by formulating an LP optimization problem. Furthermore, we apply the EPLP detector to a polar-coded MIMO system with an iterative detection and decoding (IDD) scheme. For a (128, 64) polar-coded $16\times 16$ QPSK configuration, the EPLP outperforms the state-of-the-art detectors by 1 dB at the bit error rate of $10^{-3}$ and the IDD scheme with the EPLP achieves 0.5 dB gain at the frame error rate of $10^{-5}$ with a reduced number of EP iterations.

Deep learning-based approaches have been developed to solve challenging problems in wireless communications, leading to promising results. Early attempts adopted neural network architectures inherited from applications such as computer vision. They often yield poor performance in large scale networks (i.e., poor scalability) and unseen network settings (i.e., poor generalization). To resolve these issues, graph neural networks (GNNs) have been recently adopted, as they can effectively exploit the domain knowledge, i.e., the graph topology in wireless communications problems. GNN-based methods can achieve near-optimal performance in large-scale networks and generalize well under different system settings, but the theoretical underpinnings and design guidelines remain elusive, which may hinder their practical implementations. This paper endeavors to fill both the theoretical and practical gaps. For theoretical guarantees, we prove that GNNs achieve near-optimal performance in wireless networks with much fewer training samples than traditional neural architectures. Specifically, to solve an optimization problem on an $n$ -node graph (where the nodes may represent users, base stations, or antennas), GNNs’ generalization error and required number of training samples are $\mathcal {O}(n)$ and $\mathcal {O}(n^{2})$ times lower than the unstructured multi-layer perceptrons. For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance enhancement. Extensive simulations, which cover a variety of important problems and network settings, verify our theory and the effectiveness of the proposed design framework.

Since polar were ratified as part of the 5G standard, low-complexity polar decoders with close-to-optimum error-rate performance have received significant attention. Compared to successive cancellation (SC) decoding, both SC list and SC flip decoding can improve the error rate performance by increasing the number of considered candidate solutions. The combination of both strategies leads to SC list flip (SCLF) decoding, which can provide a tradeoff between error rate performance and area as well as energy. In this letter, we derive a new flip metric for the SCLF decoding process, based on which we propose the dynamic SCLF (D-SCLF) decoding algorithm. Moreover, we exploit the distributed CRC defined in the 5G standard to further optimize the D-SCLF decoding. Numerical results show that for the downlink control channel, our D-SCLF decoder with a list size of only four and only three additional attempts can achieve the performance of a regular list decoder with a list size of eight, leading to an overall memory and average complexity (energy) reduction.

This letter proposes an iterative multiple-input multiple-output (MIMO) receiver for polar-coded system using minimum mean-square error parallel interference cancellation (MMSE-PIC) algorithm. And we propose a new kind of tree-structured Gray codes to reduce the computational complexity of MMSE-PIC without performance degradation. Simulation results indicate that, for a 64 $\times $ 4 MIMO system, the iterative detection and decoding (IDD) scheme with different decoding algorithms outperforms the separate detection and decoding (SDD) scheme 0.35 ~ 0.58 dB at the frame error rate (FER) of $10^{-3}$ when the number of iterations is four.

Immune checkpoint therapy has resulted in remarkable improvements in the outcome for certain cancers. To broaden the clinical impact of checkpoint targeting, we devised a strategy that couples targeting of the cytokine-inducible Src homology 2–containing (CIS) protein, a key negative regulator of interleukin 15 (IL-15) signaling, with fourth-generation “armored” chimeric antigen receptor (CAR) engineering of cord blood–derived natural killer (NK) cells. This combined strategy boosted NK cell effector function through enhancing the Akt/mTORC1 axis and c-MYC signaling, resulting in increased aerobic glycolysis. When tested in a lymphoma mouse model, this combined approach improved NK cell antitumor activity more than either alteration alone, eradicating lymphoma xenografts without signs of any measurable toxicity. We conclude that targeting a cytokine checkpoint further enhances the antitumor activity of IL-15–secreting armored CAR-NK cells by promoting their metabolic fitness and antitumor activity. This combined approach represents a promising milestone in the development of the next generation of NK cells for cancer immunotherapy.

Immune checkpoint therapy has resulted in remarkable improvements in the outcome for certain cancers. To broaden the clinical impact of checkpoint targeting, we devised a strategy that couples targeting of the cytokine-inducible Src homology 2–containing (CIS) protein, a key negative regulator of interleukin 15 (IL-15) signaling, with fourth-generation “armored” chimeric antigen receptor (CAR) engineering of cord blood–derived natural killer (NK) cells. This combined strategy boosted NK cell effector function through enhancing the Akt/mTORC1 axis and c-MYC signaling, resulting in increased aerobic glycolysis. When tested in a lymphoma mouse model, this combined approach improved NK cell antitumor activity more than either alteration alone, eradicating lymphoma xenografts without signs of any measurable toxicity. We conclude that targeting a cytokine checkpoint further enhances the antitumor activity of IL-15–secreting armored CAR-NK cells by promoting their metabolic fitness and antitumor activity. This combined approach represents a promising milestone in the development of the next generation of NK cells for cancer immunotherapy.

Cervical cancer (CC) remains highest in the mortality of female reproductive system cancers, while cisplatin (CDDP) resistance is the one of main reasons for the lethality. Preceding evidence has supported that karyopherins are associated with chemoresistance. In this study, we simultaneously compared CDDP-incomplete responders with CDDP-complete responders of CC patients and CDDP‐insensitive CC cell lines with CDDP‐sensitive group. We finally identified that DNA-PKcs (PRKDC) was related to CDDP sensitivity after overlapping in CC sample tissues and CC cell lines. Further functional assay revealed that targeting PRKDC by shRNA and NU7026 (specific PRKDC inhibitor) could enhance CDDP sensitivity in vitro and in vivo, which was mediated by impairing DNA damage repair pathway in CC. Mechanistically, we found that PRKDC was transcriptionally upregulated by CCAAT/enhancer-binding protein delta (CEBPD), while intriguingly, CDDP treatment strengthened the transcriptional activity of CEBPD to PRKDC. We further disclosed that Importin 4 (IPO4) augmented the nuclear translocation of CEBPD through nuclear localization signals (NLS) to activate PRKDC-mediated DNA damage repair in response to CDDP. Moreover, we demonstrated that IPO4 and CEBPD knockdown improved CDDP-induced cytotoxicity in vitro and in vivo. Together, we shed the novel insight into the role of IPO4 in chemosensitivity and provide a clinical translational potential to enhance CC chemosensitivity since the IPO4-CEBPD-PRKDC axis is actionable via NU7026 (PRKDC inhibitor) or targeting IPO4 in combination with CDDP.

Hard carbon (HC) is an attractive anode material for low-cost and high-energy density sodium-ion batteries (SIBs); however, its low initial Coulombic efficiency (ICE) limits its practical battery application. To overcome this problem, we reported a facile strategy to compensate the irreversible capacity loss of HC anodes simply by a chemical presodiation reaction of the HC electrode with a sodiation reagent (sodium biphenyl, Na-Bp). Benefiting from the strong sodiation ability of Na-Bp, HC anodes can be presodiated rapidly in a very short time and the presodiated HC (NaxHC) is found to have a desirable ICE of 100%. When coupled with the Na3V2(PO4)3cathode to build a SIB full cell, the NaxHC||Na3V2(PO4)3cell exhibits a high ICE of ∼95.0% and an elevated energy density of 218 W h kg–1, which are far superior to those of the control cell using a pristine HC anode (50% ICE and 120 W h kg–1, respectively), suggesting great advantages brought about by the chemical presodiation process. More importantly, this presodiation reaction is very mild and highly efficient and can be widely extended to a variety of Na-storage materials, offering a new route to develop high-performance Na-storage materials for practical battery applications.

ABSTRACTThe antibiotic resistome is the collection of all antibiotic resistance genes (ARGs) present in an individual. Whether an individual’s susceptibility to infection and the eventual severity of coronavirus disease 2019 (COVID-19) is influenced by their respiratory tract antibiotic resistome is unknown. Additionally, whether a relationship exists between the respiratory tract and gut ARGs composition has not been fully explored. We recruited 66 patients with COVID-19 at three disease stages (admission, progression, and recovery) and conducted a metagenome sequencing analysis of 143 sputum and 97 fecal samples obtained from them. Respiratory tract, gut metagenomes, and peripheral blood mononuclear cell (PBMC) transcriptomes are analyzed to compare the gut and respiratory tract ARGs of intensive care unit (ICU) and non-ICU (nICU) patients and determine relationships between ARGs and immune response. Among the respiratory tract ARGs, we found that Aminoglycoside, Multidrug, and Vancomycin are increased in ICU patients compared with nICU patients. In the gut, we found that Multidrug, Vancomycin, and Fosmidomycin were increased in ICU patients. We discovered that the relative abundances of Multidrug were significantly correlated with clinical indices, and there was a significantly positive correlation between ARGs and microbiota in the respiratory tract and gut. We found that immune-related pathways in PBMC were enhanced, and they were correlated with Multidrug, Vancomycin, and Tetracycline ARGs. Based on the ARG types, we built a respiratory tract-gut ARG combined random-forest classifier to distinguish ICU COVID-19 patients from nICU patients with an AUC of 0.969. Cumulatively, our findings provide some of the first insights into the dynamic alterations of respiratory tract and gut antibiotic resistome in the progression of COVID-19 and disease severity. They also provide a better understanding of how this disease affects different cohorts of patients. As such, these findings should contribute to better diagnosis and treatment scenarios.

Organic Na-host materials have are now actively pursued as an attractive alternative to conventional transition-metal compounds for development of sustainable sodium ion batteries; however, most of the organic compounds reported so far suffer from their low reversible capacity and poor cyclability. Here, we report a simple perylene diimide, 3,4,9,10-perylene-bis(dicarboximide) (PTCDI), which demonstrates remarkable electrochemical performances as an organic cathode for Na-ion batteries. With the high density of redox-active carbonyl groups in a stable π-conjugated structure, the PTCDI molecule can undergo a two-electron redox reaction with reversible insertion/extraction of 2 Na+ions per molecular unit, demonstrating a high capacity of 140 mAh g–1, a strong rate performance with a reversible capacity of 103 mAh g–1at 600 mA g–1(5 C,1 C = 120 mA g–1) and a long-term cyclability with 90% capacity retention over 300 cycles. Because this PTCDI material is commercially available and nontoxic, it may serve as a new alternative cathode for Na-ion battery applications.

Chemical prelithiation is considered to be a facile and efficient strategy to compensate the initial irreversible capacity loss of anode materials for achieving full capacity utilization of Li‐ion batteries (LIBs). However, most of the prelithiation reagents reported so far are difficult to apply for commercially used graphite (Gr) anode due to their higher redox potentials than the formation potential of graphite–lithium compounds and the co‐intercalation behavior in the interlayers of Gr anode, causing the exfoliation and destruction of Gr structure during prelithiation process. Herein, a new prelithiation solution for Gr anode by using lithium naphthalenide as a lithiation reagent and 2‐methyl tetrahydrofuran as a reduction‐tolerant solvent is demonstrated. This prelithiation solution has a very low redox potential of 0.19 V and can controllably and efficiently prelithiate Gr anode to a required degree in a few minutes without the solvent co‐intercalation. Benefiting from these advantages, the Gr anode prelithiated for 3 min exhibits a high initial Coulombic efficiency of 100%, a better rate capability, and higher cycling capacity retention than conventional pristine Gr anode. Particularly, this prelithiation solution is low cost, easily synthesized, and recycled for industrial production, thus offering a practical convenience for LIBs manufacture. Herein, a new and low‐cost prelithiation solution, lithium naphthalenide/2‐methyl tetrahydrofuran, is successfully developed to prelithiate graphite anodes. The prelithiated Gr (PGr) anode prelithiated for 3 min exhibits a high initial Coulombic efficiency (ICE) of 100%, a better rate capability, and higher cycling capacity retention than conventional pristine Gr anode. The PGr/LCO and PGr/LFP full cells demonstrate a significantly elevated ICEs and improved energy densities.

Polar codes are one of the most favorable capacity-achieving codes owing to their simple structures and low decoding complexity. Successive cancellation list (SCL) decoders with large list sizes achieve performances very close to those of maximum-likelihood (ML) decoders. However, hardware cost is a severe problem because an SCL decoder with list size Lconsists of Lcopies of a successive cancellation (SC) decoder. To address this issue, a stochastic SCL (SSCL) polar decoder is proposed. Although stochastic computing can achieve a good hardware reduction compared with the deterministic one, its straightforward application to an SCL decoder is not well-suited owing to the precision loss and severe latency. Therefore, a doubling probability approach and adaptive distributed sorting (DS) are introduced. A corresponding hardware architecture is also developed. Field programmable gate array (FPGA) results demonstrate that the proposed stochastic SCL polar decoder can achieve a good performance and complexity tradeoff.

Konopleva: Agios: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Astra Zeneca: Research Funding; Cellectis: Research Funding; Eli Lilly: Research Funding; Forty-Seven: Consultancy, Honoraria; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Calithera: Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Ablynx: Research Funding; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Kisoji: Consultancy, Honoraria; Ascentage: Research Funding. Andreeff:Senti Bio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Equity Ownership; BiolineRx: Membership on an entity's Board of Directors or advisory committees; CLL Foundation: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Equity Ownership; Breast Cancer Research Foundation: Research Funding; German Research Council: Membership on an entity's Board of Directors or advisory committees; NCI-CTEP: Membership on an entity's Board of Directors or advisory committees; Center for Drug Research & Development: Membership on an entity's Board of Directors or advisory committees; Cancer UK: Membership on an entity's Board of Directors or advisory committees; Eutropics: Equity Ownership; Aptose: Equity Ownership; Leukemia Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; NCI-RDCRN (Rare Disease Cliln Network): Membership on an entity's Board of Directors or advisory committees; CPRIT: Research Funding; NIH/NCI: Research Funding; Daiichi Sankyo, Inc.: Consultancy, Patents & Royalties: Patents licensed, royalty bearing, Research Funding; Jazz Pharmaceuticals: Consultancy; Celgene: Consultancy; Amgen: Consultancy; AstaZeneca: Consultancy; 6 Dimensions Capital: Consultancy; Reata: Equity Ownership.

Introduction:Outcomes of adverse risk acute myeloid leukemia (AML) remain dismal. Despite some morphologic remission following therapy, the majority of patients relapse and succumb to their disease. Induction chemotherapy leads to a significant reduction in tumor burden, however, resistant leukemia cells persist as minimal residual disease (MRD), the reservoir for relapse. This is likely due to the capacity of these persistent cells to hijack properties from normal hematopoietic stem cells such as self-renewal, quiescence, and recapitulation of the malignant progeny. Thus leukemia cells are functionally heterogeneous, with the majority of cells at diagnosis susceptible to chemotherapy, and a minority of resistant cells that persist despite treatment. Deeper understanding of all leukemia sub-populations is necessary in order to understand mechanisms of resistance. We hypothesized that sub-populations such as leukemia-stem cells (LSCs), and post-therapy residual cells possess identifiable, targetable characteristics that drive resistance. We performed RNA-sequencing and compared differences in gene expression between these sub-populations.