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Entry into cells is necessary for many nanomaterial applications, and a common solution is to functionalize nanoparticles (NPs) with cell-penetrating ligands. Despite intensive studies on these functionalized NPs, little is known about their effect on cellular activities to engulf other cargo from the nearby environment. Here, we use NPs functionalized with TAT (transactivator of transcription) peptide (T-NPs) as an example to investigate their impact on cellular uptake of bystander cargo. We find that T-NP internalization enables cellular uptake of bystander NPs, but not common fluid markers, through a receptor-dependent macropinocytosis pathway. Moreover, the activity of this bystander uptake is stimulated by cysteine presence in the surrounding solution. The cargo selectivity and cysteine regulation are further demonstrated ex vivo and in vivo. These findings reveal another mechanism for NP entry into cells and open up an avenue of studying the interplay among endocytosis, amino acids, and nanomaterial delivery. To enter the cells, nanomaterials often need covalent conjugation with cell-penetrating ligands such as TAT. Here, the authors show that simple mixing with TAT-coupled nanoparticles enables the cellular uptake of unfunctionalized nanoparticles, and its activity is stimulated by cysteine in the medium.

Background There is limited evidence for successful weight gain prevention interventions targeting young adults. Developing effective interventions necessitates a theoretical model that can identify barriers and enablers for healthy eating and physical activity among young adults to support weight management. This study empirically examines the utility of the COM-B model as a framework for intervention planning across two behavioural contexts: eating and physical activity. Methods A cross-sectional survey research design was employed to empirically test the COM-B model in the contexts of young adult’s eating and physical activity behaviours. Informed by the Theoretical Domains Framework, pre-validated measures appropriate for capturing the latency of the COM (Capability, Opportunity, and Motivation) constructs were sourced. Both surveys (eating and physical activity) were administered online to two independent samples of young adults aged 18–35 years. Models were specified and tested using structural equation modelling. Results A total of 582 (mean age = 22.8 years; 80.3% female) and 455 (mean age = 24.9 years; 80.8% female) participants were included in the physical activity and eating analyses, respectively. The COM-B model explained 31% of variance in physical activity behaviour and 23% of variance in eating behaviour. In the physical activity model ( N  = 582), capability and opportunity were found to be associated with behaviour through the mediating effect of motivation. In the eating model ( N  = 455), capability was found to be associated with behaviour through the mediating effect of motivation. Capability was also found to mediate the association between opportunity and motivation. Consistencies and variations were observed across both models in terms of COM indicators. Conclusions Findings support the COM-B model’s explanatory potential in the context of young adult’s physical activity and eating behaviours. Barriers and enablers underlying young adult’s physical activity and eating behaviours were identified that represent potential targets for future intervention design. Further research is needed to validate present study findings across different populations and settings.

To construct the network organizational structure of college students’ educational management and realize the informatization development of college students’ educational management, the construction method of network organizational structure of college students’ educational management is proposed based on the distributed network. MySQL and PostgreSQL are used as the bottom data structure models of the network-based organizational structure of college students’ education management, and the multisource information scheduling method, which is used to construct the transaction identifier (TID) labeling tuple model of college students’ education management network. The initial topological structure model of the network-based organizational structure nodes of college students’ education management network is constructed by the 4-tuple model, and the linear structure decomposition of the construction nodes of the network-based organizational structure of college students’ education management is carried out. The correlation characteristics of the transmission channel of the network organization structure of college students’ education management are extracted; the three-layer architecture system of data layer, network layer, and application layer is adopted; the architecture model of the network organization structure of college students’ education management is established; the multinode distributed network transmission technology is used to build a tree structure model, so as to realize the design of the network organization structure model of college students’ education management and the scheduling of multi-source information parameters; and the fuzzy multi-attribute decision-making method to realize the adaptive scheduling of the network organization structure of college students’ education management is adopted, so as to improve the storage management, fault tolerance, and safe access ability of the network organization structure of college students’ education management. The test results show that the network organizational structure of college students’ educational management is designed by this method, which improves the ability of information transmission and scheduling and improves the informatization level of college students’ educational management.

The incidence of adverse effects and pathogen resistance encountered with small molecule antibiotics is increasing. As such, there is mounting focus on immunogene therapy to augment the immune system’s response to infection and accelerate healing. A major obstacle to in vivo gene delivery is that the primary uptake pathway, cellular endocytosis, results in extracellular excretion and lysosomal degradation of genetic material. Here we show a nanosystem that bypasses endocytosis and achieves potent gene knockdown efficacy. Porous silicon nanoparticles containing an outer sheath of homing peptides and fusogenic liposome selectively target macrophages and directly introduce an oligonucleotide payload into the cytosol. Highly effective knockdown of the proinflammatory macrophage marker IRF5 enhances the clearance capability of macrophages and improves survival in a mouse model of Staphyloccocus aureus pneumonia. In the context of increasing bacterial antibiotic-resistance, gene therapy that targets the immune system to clear infection is a major goal. Here the authors show a silicon based nanosystem that modulates the macrophage response in an in vivo model of Staphylococcal pneumonia.

HighlightsPreparation strategies of cellulose nanopaper were elaborated.Functionalization of cellulose nanopaper and its advanced applications were summarized.Prospects and challenges of cellulose nanopaper were discussed.Cellulose nanopaper has shown great potential in diverse fields including optoelectronic devices, food packaging, biomedical application, and so forth, owing to their various advantages such as good flexibility, tunable light transmittance, high thermal stability, low thermal expansion coefficient, and superior mechanical properties. Herein, recent progress on the fabrication and applications of cellulose nanopaper is summarized and discussed based on the analyses of the latest studies. We begin with a brief introduction of the three types of nanocellulose: cellulose nanocrystals, cellulose nanofibrils and bacterial cellulose, recapitulating their differences in preparation and properties. Then, the main preparation methods of cellulose nanopaper including filtration method and casting method as well as the newly developed technology are systematically elaborated and compared. Furthermore, the advanced applications of cellulose nanopaper including energy storage, electronic devices, water treatment, and high-performance packaging materials were highlighted. Finally, the prospects and ongoing challenges of cellulose nanopaper were summarized.

Ferroptosis is a form of cell death characterized by lipid peroxidation. Previous studies have reported that knockout of NF-κB activating protein (NKAP), an RNA-binding protein, increased lipid peroxidation level in naive T cells and induced cell death in colon cancer cells. However, there was no literature reported the relationship between NKAP and ferroptosis in glioblastoma cells. Notably, the mechanism of NKAP modulating ferroptosis is still unknown. Here, we found NKAP knockdown induced cell death in glioblastoma cells. Silencing NKAP increased the cell sensitivity to ferroptosis inducers both in vitro and in vivo. Exogenous overexpression of NKAP promoted cell resistance to ferroptosis inducers by positively regulating a ferroptosis defense protein, namely cystine/glutamate antiporter (SLC7A11). The regulation of SLC7A11 by NKAP can be weakened by the m 6 A methylation inhibitor cycloleucine and knockdown of the m 6 A writer METTL3. NKAP combined the “RGAC” motif which was exactly in line with the m 6 A motif “RGACH” (R = A/G, H = A/U/C) uncovered by the m 6 A-sequence. RNA Immunoprecipitation (RIP) and Co-Immunoprecipitation (Co-IP) proved the interaction between NKAP and m 6 A on SLC7A11 transcript. Following its binding to m 6 A, NKAP recruited the splicing factor proline and glutamine-rich (SFPQ) to recognize the splice site and then conducted transcription termination site (TTS) splicing event on SLC7A11 transcript and the retention of the last exon, screened by RNA-sequence and Mass Spectrometry (MS). In conclusion, NKAP acted as a new ferroptosis suppressor by binding to m 6 A and then promoting SLC7A11 mRNA splicing and maturation.

Background Interventions aiming to promote active school travel (AST) are being implemented globally to reverse AST decline. This systematic literature provides an update of AST interventions assessing study quality and theory use to examine progress in the field. Methods A systematic review was conducted to identify and analyse AST interventions published between 2010 and 2016. Seven databases were searched and exclusion criteria were applied to identify 18 AST interventions. Interventions were assessed using the Active Living by Design (ALBD) Community Action (5P) Model and the Evaluation of Public Health Practice Projects (EPHPP). Methods used to evaluate the effectiveness of each intervention and their outcomes and extent of theory use were examined. Results Seven out of 18 studies reported theory use. The analysis of the interventions using the ALBD Community Action Model showed that Preparation and Promotion were used much more frequently than Policy and Physical projects. The methodological quality 14 out of 18 included interventions were assessed as weak according to the EPHPP framework. Conclusion Noted improvements were an increase in use of objective measures. Lack of theory, weak methodological design and a lack of reliable and valid measurement were observed. Given that change is evident when theory is used and when policy changes are included extended use of the ALBD model and socio-ecological frameworks are recommended in future.

Electrified vehicles (EV) and marine vessels represent promising clean transportation solutions to reduce or eliminate petroleum fuel use, greenhouse gas emissions and air pollutants. The presently commonly used electric energy storage system (ESS) is based on lithium-ion batteries. These batteries are the electrified or hybridized powertrain’s most expensive component and show noticeable performance degradations under different use patterns. Therefore, battery life prediction models play a key role in realizing globally optimized EV design and energy control strategies. This research studies the data-driven modelling and prediction methods for Li-ion batteries’ performance degradation behaviour and the state of health (SOH) estimation. The research takes advantage of the increasingly available battery test and data to reduce prediction errors of the widely used semi-empirical modelling methods. Several data-driven modelling techniques have been applied, improved, and compared to identify their advantages and limitations. The data-driven approach and Kalman Filter (KF) algorithm are used to estimate and predict the degradation of the battery during operation. The combined algorithm of Gaussian Process Regression (GPR) and Extended Kalman Filter (EKF) showed higher accuracy than other algorithms.

Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, utilizing remote sensing data and limited historical inundation records. In this study, two ensemble learning algorithms, Random Forest (RF) and XGBoost, were adopted to assess the flood susceptibility of Kunming, a typical mountainous urban area prone to severe flood disasters. A flood inventory was created using flood observations from 2018 to 2022. The spatial database included 10 explanatory factors, encompassing climatic, geomorphic, and anthropogenic factors. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were selected for model comparison. To minimize the influence of expert opinions on model training, this study employed a strategy of uniformly random sampling in historically non-flooded areas for negative sample selection. The results demonstrated that (1) ensemble learning algorithms offer higher accuracy than other machine learning methods, with RF achieving the highest accuracy, evidenced by an area under the curve (AUC) of 0.87, followed by XGBoost at 0.84, surpassing both ANN (0.83) and SVM (0.82); (2) the interpretability of ensemble learning highlighted the differences in the potential distribution of the training data’s positive and negative samples. Feature importance in ensemble learning can be utilized to minimize human bias in the collection of flooded-site samples, more targeted flood susceptibility maps of the study area’s road network were obtained; and (3) ensemble learning algorithms exhibited greater stability and robustness in datasets with varied negative samples, as evidenced by their performance in F1-Score, Kappa, and AUC metrics. This paper further substantiates the superiority of ensemble learning in flood susceptibility assessment tasks from the perspectives of accuracy, interpretability, and robustness, enhances the understanding of the impact of negative samples on such assessments, and optimizes the specific process for urban flood susceptibility assessment using data-driven methods.

Countries around the world have paid increasing attention to the issue of marine security, and sea target detection is a key task to ensure marine safety. Therefore, it is of great significance to propose an efficient and accurate sea-surface target detection algorithm. The anchor-setting method of the traditional YOLO v3 only uses the degree of overlap between the anchor and the ground-truth box as the standard. As a result, the information of some feature maps cannot be used, and the required accuracy of target detection is hard to achieve in a complex sea environment. Therefore, two new anchor-setting methods for the visual detection of sea targets were proposed in this paper: the average method and the select-all method. In addition, cross PANet, a feature fusion structure for cross-feature maps was developed and was used to obtain a better baseline cross YOLO v3, where different anchor-setting methods were combined with a focal loss for experimental comparison in the datasets of sea buoys and existing sea ships, SeaBuoys and SeaShips, respectively. The results showed that the method proposed in this paper could significantly improve the accuracy of YOLO v3 in detecting sea-surface targets, and the highest value of mAP in the two datasets is 98.37% and 90.58%, respectively.