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Objectives: To evaluate the manufacturability, efficacy and safety of allogeneic CD19 chimeric antigen receptor double‐negative T cells (CD19‐CAR‐DNTs) as an off‐the‐shelf therapeutic cell product. Methods: A membrane proteome array was used to assess the off‐target binding of CD19‐CAR. DNTs derived from healthy donors were transduced with lentiviral vectors encoding the CD19‐CAR. The manufacture of the CD19‐CAR‐DNTs was under GMP conditions, and their surface molecule expression patterns were characterised using flow cytometry. We investigated the off‐the‐shelf potential of CD19‐CAR‐DNTs by evaluating the cryopreserved CD19‐CAR‐DNTs in terms of cell viability as well as their cytotoxicity against various CD19+ target cell lines and primary patient blasts in vitro. We evaluated the persistence and safety of the cryopreserved CD19‐CAR‐DNTs in xenograft models in vivo. Results: GMP‐grade CD19‐CAR‐DNTs were manufactured and cryopreserved for use in advance. The cryopreserved CD19‐CAR‐DNTs maintain their viability and antitumor activity against various CD19+ target cell lines and primary patient blasts. These cells significantly prolonged the survival in Raji‐Luc‐xenografted NOG mice. Multiple infusions of the cells can further augment their efficacy. Remarkably, following a single infusion in mice, CD19‐CAR‐DNTs rapidly got distributed among well‐perfused organs initially, and progressively spread to most tissues, peaking at Day 43. In toxicity studies, CD19‐CAR‐DNTs significantly reduced tumor burden and ameliorated tissue damage in tumor‐bearing NOG mice. Critically, no immunotoxicity or graft versus host disease was observed in non‐tumor‐bearing NOG mice. Conclusions: The allogeneic CD19‐CAR‐DNTs fulfil the requirements of an off‐the‐shelf therapeutic cell product, offering a promising new approach to the treatment of haematological malignancies. [ABSTRACT FROM AUTHOR]

A novel approach is proposed in this paper to reconstruct the far‐field radiation pattern from the phaseless electric field of an antenna scanned on a single near‐field sphere. It adopts the dipole equivalence approach to project the near‐field electric field into a spherically distributed array of electric dipoles. A term representing the error from the linearly correlated portion in the least‐square problem associated with the dipole equivalence, namely the linear correlation error, is introduced. It is demonstrated that by iterative search to minimize the linear correlation error using the covariance matrix adaptation evolution strategy, near‐field phase distributions can be found efficiently from the magnitude‐only near‐field, and the far‐field radiation pattern can be computed. Two representative case studies are given here to validate the proposed method. Results demonstrate good agreements between computations and simulations. [ABSTRACT FROM AUTHOR]

Due to the advantages of parallel architecture and low power consumption, a field-programmable gate array (FPGA) is typically utilized as the hardware for convolutional neural network (CNN) accelerators. However, SRAM-based FPGA devices are extremely susceptible to single-event upsets (SEUs) induced by space radiation. In this paper, a fault tolerance analysis and fault injection experiments are applied to a CNN accelerator, and the overall results show that SEUs occurring in a control unit (CTRL) lead to the highest system error rate, which is over 70%. After that, a hybrid hardening strategy consisting of a finite state machine error-correcting circuit (FSM-ECC) and a triple modular redundancy automatic hardening technique (TMR-AHT) is proposed in this paper to achieve a tradeoff between radiation reliability and design overhead. Moreover, the proposed methodology has very small workload and good migration ability. Finally, by full exploiting the fault tolerance property of CNNs, a highly reliable CNN accelerator with the proposed hybrid hardening strategy is implemented with Xilinx Zynq-7035. When BER is 2 × 10−6, the proposed hybrid hardening strategy reduces the whole system error rate by 78.95% with the overhead of an extra 20.7% of look-up tables (LUTs) and 20.9% of flip-flops (FFs). [ABSTRACT FROM AUTHOR]

This paper examines the impact of air pollution on the media slant of publicly listed firms in China. Using a large panel of air quality and media data at the city level, we find that lower air quality generally leads to a more negative media slant. When the air quality falls from lightly polluted to heavily polluted, the number of negative sentences in a news article increases by about 1%. Our subsample analysis shows that the effect of air pollution on media slant is similar for news articles covering large and small firms, SOE and non-SOE firms and for official and non-official newspaper articles. Furthermore, the effect of air pollution on media slant is stronger for firms in heavy polluting industries. These results suggest that air pollution affects media slant. [ABSTRACT FROM AUTHOR]

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC–MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration. • Amino acid metabolism closely correlates with future liver remnant growth. • MUVR selects a more concise set of amino acids compared to OPLS-VIP. • The SVM and KNN consensus is superior to all base estimators and other consensuses. [ABSTRACT FROM AUTHOR]

Background: Posthepatectomy liver failure (PHLF) is the leading cause of mortality in patients undergoing hepatectomy. However, practical models for accurately predicting the risk of PHLF are lacking. This study aimed to develop precise prediction models for clinically significant PHLF., Methods: A total of 226 patients undergoing hepatectomy at a single center were recruited. The study outcome was clinically significant PHLF. Five preoperative and postoperative machine learning (ML) models were developed and compared with four clinical scores, namely, the MELD, FIB-4, ALBI, and APRI scores. The robustness of the developed ML models was internally validated using fivefold cross-validation (CV) by calculating the average of the evaluation metrics and was externally validated on an independent temporal dataset, including the area under the curve (AUC) and the area under the precision-recall curve (AUPRC). SHapley Additive exPlanations analysis was performed to interpret the best performance model., Results: Clinically significant PHLF was observed in 23 of 226 patients (10.2%). The variables in the preoperative model included creatinine, total bilirubin, and Child-Pugh grade. In addition to the above factors, the extent of resection was also a key variable for the postoperative model. The preoperative and postoperative artificial neural network (ANN) models exhibited excellent performance, with mean AUCs of 0.766 and 0.851, respectively, and mean AUPRC values of 0.441 and 0.645, whereas the MELD, FIB-4, ALBI, and APRI scores reached AUCs of 0.714, 0.498, 0.536, and 0.551, respectively, and AUPRC values of 0.204, 0.111, 0.128, and 0.163, respectively. In addition, the AUCs of the preoperative and postoperative ANN models were 0.720 and 0.731, respectively, and the AUPRC values were 0.380 and 0.408, respectively, on the temporal dataset., Conclusion: Our online interpretable dynamic ML models outperformed common clinical scores and could function as a clinical decision support tool to identify patients at high risk of PHLF preoperatively and postoperatively., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)