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In this study, we compared the similarities and differences in adverse events (AEs) among CAR T-cell products through signal mining via the FDA Adverse Event Reporting System (FAERS) and identified unknown AEs to provide a reference for safe clinical medication. Data from the FAERS database spanning from the fourth quarter of 2017 to the first quarter of 2024 were extracted. Signals were identified using the reporting odds ratio (ROR) method and the Medicines and Healthcare Products Regulatory Agency (MHRA) method. A total of 11,386 AE reports related to six CAR T-cell products were selected. The top three categories of AEs reported were nervous system disorders, immune system disorders, and general disorders and administration site conditions. However, there were variations in the AE spectra among the different CAR T-cell products. The BCMA-targeting drugs idecabtagene vicleucel (Ide-cel) and ciltacabtagene autoleucel (Cilta-cel) were found to be associated with parkinsonism, which were not observed in CD19-targeting drugs. Tisagenlecleucel (Tisa-cel) and axicabtagene ciloleucel (Axi-cel) exhibited cerebrovascular accident-related AEs, graft versus host disease, and abnormal coagulation indices. Cilta-cel was associated with cerebral hemorrhage, intracranial hemorrhage, cranial nerve disorder, and facial nerve disorder. Cardiopulmonary toxicity, including hypoxia, tachypnoea, cardiorenal syndrome, and hypotension, exhibited strong signal intensities and considerable overlap with CRS. The number of positive signals for cardiopulmonary toxicity associated with drugs targeting CD-19 is greater. Clinicians should assess patients prior to medication and closely monitor their vital signs, mental status, and laboratory parameters during treatment. [ABSTRACT FROM AUTHOR]

Exploring an efficient electromagnetic protection strategy for buildings is of great significance to solve the problems caused by increasing electromagnetic pollution, as the rapid progress of technology continues. In this work, FeCo alloy/carbon–carbon nanotube (FeCo/C–CNT) nanocomposites, with significant microwave absorption performance, were successfully synthesized using a simple pyrolysis method involving FeCo–ZIF MOFs precursors and added to epoxy resin to prepare a novel electromagnetic wave absorption (EWA) coating. The minimum reflection loss (RLmin) of the coating applied on the surface of the ceramic tiles was −23.89 dB at 11.37 GHz and the effective absorption bandwidth (EAB) reached 8.85 GHz. Through microscopic characterization and analysis of the electromagnetic parameters of the FeCo/C–CNT nanocomposites, it was found that the EWA coating has an ultrabroad band wave absorption effect, mainly due to the comprehensive advantages of the polarization loss from CNTs, impedance matching, the dual loss synergy effect, and multiple reflection between the FeCo alloys, the carbon layer, and the CNTs. This study has successfully developed high-performance EWA materials and demonstrated the feasibility of an EWA coating applied to building surfaces, contributing to the improvement of electromagnetic protection functions of buildings. [ABSTRACT FROM AUTHOR]

Reductive soil disinfestation (RSD) and chemical soil fumigation (CSF) comprise the most popular pre-planting soil management strategies. Their efficiency in suppressing several plant diseases in agricultural production systems has been compared. However, the disease-control effect of these methods on Fusarium wilt disease in Dioscorea batatas Decne (D. batatas) remains unclear. Importantly, dissimilarities in the impact of their bio-predictors on plant health have not been well characterized. Herein, four treatments, including no treatment (CK), RSD with gran chaff (GC-RSD) and molasses (MO-RSD), and CSF with dazomet (DA-CSF), were performed in a pot experiment using D. batatas-diseased soil. Compared with the CK treatment, the Fusarium oxysporum population significantly decreased by 88.89–97.78% following the DA-CSF, GC-RSD, and MO-RSD treatments. The bacterial community and functional composition of the soil were considerably altered by these treatments. However, the incidence of Fusarium wilt disease in D. batatas was significantly decreased in the two RSD-treated soils, rather than in DA-CSF-treated soils. Bacterial α-diversity and population as well as some key nitrogen-related functional gene expressions as bio-predictors were significantly lower in DA-CSF-treated soil than in RSD-treated soil. In particular, the core (e.g., Azotobacter, Phenylobacterium, Clostridium, Bradyrhizobium, Microvirga, and Caulobacter) and unique (e.g., Pseudomonas, Brevundimonas, Flavobacterium, Ochrobactrum, and Sphingobacterium) functional microbiomes in RSD-treated soil exerted a positive impact on soil functional composition of the soil and plant growth. Taken together, our results indicate that RSD outperformed CSF in promoting plant health by regulating the bacterial community and functional composition. [ABSTRACT FROM AUTHOR]

Global climate change and rapid socio-economic development have increased the uncertainty in water resource systems and the complexity of water risk issues. Analyzing water risk and its spatial distribution is integral to the attainment of Sustainable Development Goal (SDG) 6, as this contributes to effective water resource partition management. In this paper, a compiling method of risk atlas with multiple layers is proposed, and the water risk system is divided into five sub-systems including the risk of resource, management, engineering, quality, and disaster. The information used for the risk atlas is calculated by a risk evaluation model based on a Pressure–State–Response (PSR) framework, hierarchical cluster, and set pair analysis (SPA). Risks in the Qinling Mountains of Shaanxi (as a case study) are evaluated and visualized. The results show that grades IV and V of engineering, disaster, and resource risk exceed 40%, indicating that they require prior control. The quality and management risks are not major, but there is still room for improvement. Overall, the risk atlas can effectively and objectively reflect the spatial distribution of water risk and provide a basis for the layout of water risk control measures. [ABSTRACT FROM AUTHOR]

In this study, efficient remediation of p-chloroaniline (PCA)-contaminated soil by activated persulfate (PS) using nanosized zero-valent iron/biochar (B-nZVI/BC) through the ball milling method was conducted. Under the conditions of 4.8 g kg−1 B-nZVI/BC and 42.0 mmol L−1 PS with pH 7.49, the concentration of PCA in soil was dramatically decreased from 3.64 mg kg−1 to 1.33 mg kg−1, which was much lower than the remediation target value of 1.96 mg kg−1. Further increasing B-nZVI/BC dosage and PS concentration to 14.4 g kg−1 and 126.0 mmol L−1, the concentration of PCA was as low as 0.15 mg kg−1, corresponding to a degradation efficiency of 95.9%. Electron paramagnetic resonance (EPR) signals indicated SO4•−, •OH, and O2•− radicals were generated and accounted for PCA degradation with the effect of low-valence iron and through the electron transfer process of the sp2 hybridized carbon structure of biochar. 1-chlorobutane and glycine were formed and subsequently decomposed into butanol, butyric acid, ethylene glycol, and glycolic acid, and the degradation pathway of PCA in the B-nZVI/BC-PS system was proposed accordingly. The findings provide a significant implication for cost-effective and environmentally friendly remediation of PCA-contaminated soil using a facile ball milling preparation of B-nZVI/BC and PS. [ABSTRACT FROM AUTHOR]

Driver distraction detection (3D) is essential in improving the efficiency and safety of transportation systems. Considering the requirements for user privacy and the phenomenon of data growth in real-world scenarios, existing methods are insufficient to address four emerging challenges, i.e., data accumulation, communication optimization, data heterogeneity, and device heterogeneity. This paper presents an incremental and cost-efficient mechanism based on federated meta-learning, called ICMFed, to support the tasks of 3D by addressing the four challenges. In particular, it designs a temporal factor associated with local training batches to stabilize the local model training, introduces gradient filters of each model layer to optimize the client–server interaction, implements a normalized weight vector to enhance the global model aggregation process, and supports rapid personalization for each user by adapting the learned global meta-model. According to the evaluation made based on the standard dataset, ICMFed can outperform three baselines in training two common models (i.e., DenseNet and EfficientNet) with average accuracy improved by about 141.42%, training time saved by about 54.80%, communication cost reduced by about 54.94%, and service quality improved by about 96.86%. [ABSTRACT FROM AUTHOR]

Al/PTFE has the advantages of high impact-responsive energy release, appropriate sensitivity, a fast energy release rate, and high energy density, and it is increasingly widely being used in the field of ammunition. In this paper, based on the traditional formula Al/PTFE (26.5%/73.5%), the reinforced Al/PTFE active materials are prepared by the process of cold pressing, sintering, and rapid cooling. Quasi static and dynamic compression experiments were carried out under different compression pressures (200~800 MPa), strain rates (0.002 s−1, 0.02 s−1, 1400~3300 s−1), and temperatures (23 °C, −20 °C, −30 °C, −40 °C). The effects of pressure, strain rate, and temperature on the quasi-static and dynamic compression properties of Al/PTFE materials are analyzed. The results show that the reinforced Al/PTFE specimens show a significant correlation between temperature and strain rate. Based on the classical Zhu–Wang–Tang (ZWT) constitutive model, the ZWT constitutive model parameters of the reinforced Al/PTFE active materials under different pressing pressures at room temperature and the ZWT constitutive model parameters of the reinforced Al/PTFE active materials at low temperature are obtained by fitting, respectively. The accuracy of the constitutive model parameters (elastic modulus, stress relaxation time, and thermal softening index) is verified. In this paper, a constitutive model considering both temperature and strain rate effects is established in order to provide reference for the study of mechanical properties of active materials. [ABSTRACT FROM AUTHOR]

Replacing the commercial graphite anode in Li-ion batteries with pseudocapacitor materials is an effective way to obtain high-performance energy storage devices. α-MoO3 is an attractive pseudocapacitor electrode material due to its theoretical capacity of 1117 mAh g−1. Nevertheless, its low conductivity greatly limits its electrochemical performance. MXene is often used as a 2D conductive substrate and flexible framework for the development of a non-binder electrode because of its unparalleled electronic conductivity and excellent mechanical flexibility. Herein, a free-standing α-MoO3/MXene composite anode with a high specific capacity and an outstanding rate capability was prepared using a green and simple method. The resultant α-MoO3/MXene composite electrode combines the advantages of each of the two components and possesses improved Li+ diffusion kinetics. In particular, this α-MoO3/MXene free-standing electrode exhibited a high Li+ storage capacity (1008 mAh g−1 at 0.1 A g−1) and an outstanding rate capability (172 mAh g−1 at 10 A g−1), as well as a much extended cycling stability (500 cycles at 0.5 A g−1). Furthermore, a full cell was fabricated using commercial LiFePO4 as the cathode, which displayed a high Li+ storage capacity of 160 mAh g−1 with an outstanding rate performance (48 mAh g−1 at 1 A g−1). We believe that our research reveals new possibilities for the development of an advanced free-standing electrode from pseudocapacitive materials for high-performance Li-ion storage. [ABSTRACT FROM AUTHOR]

In order to manage regional water resources efficiently and sustainably and promote the rational utilization of water resources, it is necessary to evaluate the water-supply benefit reasonably. On the basis of emergy theory, this paper constructs the water-supply-benefit model of economic (industry, agriculture, and the tertiary industry) and social (domestic, employment security, entertainment, scientific research) systems. Taking Xi'an from 2014 to 2020 as an example, by analyzing the energy flow of each system and the multisource water transformities, the water contribution rate, the water-supply benefit, and the unit-water-resource value in each system are calculated. For the water-supply benefits: Industry > Agriculture > Domestic > Tertiary industry > Employment Security > Entertainment > Scientific research. For the unit-water-resource values: Industry > Tertiary industry > Agriculture > Domestic > Entertainment > Employment security > Scientific research. In the economic system, the water-supply benefit and the unit-water value of industry were always the largest, followed by agriculture and the tertiary industry. However, the Pearson correlation coefficient between the water contribution rate and the output of the industrial system was only 0.52, which was less than that of other production industries, which indicates that there might be a waste of water and that industrial water conservation needs to be further strengthened. In the social system, the domestic-water-supply benefits and the water-resource value were the largest. This is because water resources, as a basic resource, always affect people's health and quality of life. [ABSTRACT FROM AUTHOR]

Visual question answering (VQA) is a multi-modal task involving natural language processing (NLP) and computer vision (CV), which requires models to understand of both visual information and textual information simultaneously to predict the correct answer for the input visual image and textual question, and has been widely used in smart and intelligent transport systems, smart city, and other fields. Today, advanced VQA approaches model dense interactions between image regions and question words by designing co-attention mechanisms to achieve better accuracy. However, modeling interactions between each image region and each question word will force the model to calculate irrelevant information, thus causing the model's attention to be distracted. In this paper, to solve this problem, we propose a novel model called Multi-modal Explicit Sparse Attention Networks (MESAN), which concentrates the model's attention by explicitly selecting the parts of the input features that are the most relevant to answering the input question. We consider that this method based on top-k selection can reduce the interference caused by irrelevant information and ultimately help the model to achieve better performance. The experimental results on the benchmark dataset VQA v2 demonstrate the effectiveness of our model. Our best single model delivers 70.71% and 71.08% overall accuracy on the test-dev and test-std sets, respectively. In addition, we also demonstrate that our model can obtain better attended features than other advanced models through attention visualization. Our work proves that the models with sparse attention mechanisms can also achieve competitive results on VQA datasets. We hope that it can promote the development of VQA models and the application of artificial intelligence (AI) technology related to VQA in various aspects. [ABSTRACT FROM AUTHOR]

Four new sesquiterpenoids (1–4) and six known sesquiterpenoids (5–10), were isolated from the EtOAc phase of the ethanolic extract of Ainsliaea yunnanensis. Their structures were established by spectroscopic methods, including 1-D, 2-D NMR and HPLC-MS. All compounds were tested for their anti-inflammatory effect by the inhibition of the activity of NLRP3 inflammasome by blocking the self-slicing of pro-caspase-1, which is induced by nigericin, then the secretion of mature IL-1β, mediated by caspase-1, was suppressed. Unfortunately none of the compounds showed an anti-inflammatory effect. [ABSTRACT FROM AUTHOR]