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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]

Over the last 40 years, a burrowing mammal eradication policy has been prevalent on the Qinghai–Tibetan Plateau (QTP). This policy is based on similar burrowing mammal eradication programs in other areas and is justified on the assumptions that burrowing mammals compete with livestock for forage and contribute to grassland degradation. However, there is no clear theoretical or experimental evidence supporting these assumptions.This paper synthesizes the ecological functioning of small burrowing mammals in natural grasslands and discusses the irrationality and consequences of burrowing mammal eradication for sustainable livestock grazing and grassland degradation.Past burrowing mammal eradication efforts have failed because increased food availability for the remaining rodents and reduced predator populations led to rapid population rebounds. Herbivores differ in diet, and there is clear evidence that burrowing mammals, especially plateau zokors Myospalax baileyi, have a different diet than livestock. In QTP meadows, burrowing mammal eradication induces a shift towards plant communities with fewer species preferred by livestock and more species preferred by burrowing mammals. Thus, eradicating burrowing mammals has the opposite effect, a reduction in livestock preferred vegetation. We suggest that the policy of poisoning burrowing mammals needs to be reconsidered and revoked as soon as possible.We argue that incorporating density‐dependent factors such as predation and food availability are essential for maintaining a low burrowing mammal density. For degraded grasslands, we suggest that the optimal sustainable approach is to decrease the intensity of livestock grazing. Lower grazing induces changes in vegetation structure and plant species composition that increases predation on burrowing mammals and decreases the abundance of plants preferred by burrowing mammals. Such a nature‐based grassland management system maintains the density of burrowing mammals at a low stable density while minimizing human management and interventions. [ABSTRACT FROM AUTHOR]

Ecological stability has long been considered to change over succession, but how secondary succession influences the relationship between diversity and temporal stability of biomass production at different spatial scales is poorly understood.We studied changes in plant diversity, functional temporal stability (biomass production) and compositional temporal stability (the latter two are hereafter referred to as functional stability and compositional stability) and explored the stabilizing roles of plant diversity at two spatial scales (small plots of 0.25 m2 and large transects of 1.25 m2) during secondary succession in a subalpine meadow from 2003 to 2010.Our results showed that both plant diversity and functional and compositional stability increased at the small plot scale and large transect scale during secondary succession. As secondary succession proceeded, higher average alpha diversity (i.e. species diversity at the plot scale) led to higher functional and compositional stability at the plot scale by mainly species stability, predominantly contributing to higher functional and compositional stability at the large transect scale. In addition, Simpson‐based beta diversity (i.e. compositional dissimilarity among communities within the same transect), while unaffected by succession, contributed to functional stability at the large transect scale by promoting asynchronous dynamics among communities.Synthesis. Our study highlights the stabilizing effects of plant diversity across the two spatial scales during secondary succession. Our findings provide the first empirical evidence that biodiversity‐mediated effects on ecosystem temporal stability strengthen over successional time, suggesting that the stabilizing effects of biodiversity should be considered across spatial and temporal scales in the face of global changes and biodiversity loss. [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.)

Objective: Amino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH)., Methods: The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography-tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index., Results: Eleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively., Conclusion: The KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yan, Chen, Dai, Xiang, Long, Wu, Jiang, Zou, Wang and Zhu.)

Purpose: This study aimed to explore the efficacy of the computed tomography (CT) radiomics model for predicting the Ki-67 proliferation index (PI) of pure-solid non-small cell lung cancer (NSCLC)., Materials and Methods: This retrospective study included pure-solid NSCLC patients from five centers. The radiomics features were extracted from thin-slice, non-enhanced CT images of the chest. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to reduce and select radiomics features. Logistic regression analysis was employed to build predictive models to determine Ki-67-high and Ki-67-low expression levels. Three prediction models were established: the clinical model, the radiomics model, and the nomogram model combining the radiomics signature and clinical features. The prediction efficiency of different models was evaluated using the area under the curve (AUC)., Results: A total of 211 NSCLC patients with pure-solid nodules or masses were included in the study (N=117 for the training cohort, N=49 for the internal validation cohort, and N=45 for the external validation cohort). The AUC values for the clinical models in the training, internal validation, and external validation cohorts were 0.73 (95% CI: 0.64-0.82), 0.75 (95% CI:0.62-0.89), and 0.72 (95% CI: 0.57-0.86), respectively. The radiomics models showed good predictive ability in diagnosing Ki-67 expression levels in the training cohort (AUC, 0.81 [95% CI: 0.73-0.89]), internal validation cohort (AUC, 0.81 [95% CI: 0.69-0.93]) and external validation cohort (AUC, 0.78 [95% CI: 0.64-0.91]). Compared to the clinical and radiomics models, the nomogram combining both radiomics signatures and clinical features had relatively better diagnostic performance in all three cohorts, with the AUC of 0.83 (95% CI: 0.76-0.90), 0.83 (95% CI: 0.71-0.94), and 0.81 (95% CI: 0.68-0.93), respectively., Conclusion: The nomogram combining the radiomics signature and clinical features may be a potential non-invasive method for predicting Ki-67 expression levels in patients with pure-solid NSCLC., Competing Interests: Author HL was employed by the company GE Healthcare. The remaining authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Li, Xiang, Li, Li, Huang, Zeng, Lin, Fang and Yang.)

Background: Metastatic colorectal cancer (mCRC) is one of the most common and deadly cancers worldwide. For most patients diagnosed with mCRC and managed with 5-fluorouracil (5-FU)/leucovorin plus oxaliplatin (FOLFOX), the median survival time is still less than 2 years. Small molecule selective vascular endothelial growth factor receptor (VEGFR) inhibitors have been demonstrated to have strong anti-tumour activity in various cancer models., Objective: To demonstrate the efficacy and safety of selective VEGFR inhibitors in the management of mCRC., Methods: A comprehensive search in PubMed, EMBASE, Web of Science, Ovid MEDLINE, Google Scholar, Springer and Cochrane Central databases was performed for randomized controlled trials (RCTs) focusing on the effect of selective VEGFR inhibitors on mCRC. The primary outcome measures were progression-free survival (PFS) rates, overall survival (OS) rates, complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), objective response rates (ORRs), disease control rates (DCRs) and adverse effect (AE) rates. The dates of the included studies ranged from the inception of the database to January 15, 2020., Results: Twenty-two RCTs were included. A total of 9362 patients met the inclusion criteria. Compared with placebo, selective VEGFR inhibitors significantly increased the PFS rate, SD, PR and DCR, reduced PD, caused more treatment-emergent adverse events (TEAEs), hypertension, hand-foot skin reaction, diarrhoea, fatigue, and thrombocytopaenia and increased aspartate aminotransferase(AST) concentration. There was no significant difference between selective VEGFR inhibitors and placebo regarding OS rate, CR, ORR, proteinuria, hyperbilirubinaemia or alkaline phosphatase(ALP) concentration. Additionally, compared with FOLFOX4+placebo, FOLFOX4+ selective VEGFR inhibitors, clearly reduced PD, and caused more 3-4 AEs, serious AEs, hypertension, hand-foot syndrome, diarrhoea, nausea, vomiting, decreased appetite, dehydration, fatigue, dizziness, neutropaenia and thrombocytopaenia. For PFS rate, OS rate, CR, PR, SD, ORR, abdominal pain, peripheral sensory neuropathy, asthaenia, anaemia and hypokalaemia rates, there was no significant difference between FOLFOX4+ selective VEGFR inhibitors and FOLFOX4+placebo. However, compared with FOLFOX4+bevacizumab, FOLFOX4+selective VEGFR inhibitors, led to increased hypertension, neutropaenia, fatigue, thrombocytopaenia and asthaenia. There is no clear difference between FOLFOX4+selective VEGFR inhibitors and FOLFOX4+ bevacizumab with regard to PFS rate, OS rate, CR, PR, SD, PD, ORR, diarrhoea, nausea, vomiting, peripheral neuropathy and abdominal pain rates. Selective VEGFR inhibitors+cetuximab increased PFS and PR and reduced PD compared to cetuximab, but there was no statistical difference between the two groups for OS and SD., Conclusion: Compared with placebo or cetuximab, selective VEGFR inhibitors alone or combined with cetuximab seemed to be more efficacious for mCRC respectively; however, the effects were not better than FOLFOX4 alone or when combined with bevacizumab for mCRC. Additionally, selective VEGFR inhibitors were not as safe as placebo or FOLFOX4 alone or in combination with bevacizumab in mCRC., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)