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PurposeRapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.Design/methodology/approachAn extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.FindingsThe results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.Originality/valueThis study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

Neuromorphic electronics, which use artificial photosensitive synapses, can emulate biological nervous systems with in-memory sensing and computing abilities. Benefiting from multiple intra/interactions and strong light-matter coupling, two-dimensional heterostructures are promising synaptic materials for photonic synapses. Two primary strategies, including chemical vapor deposition and physical stacking, have been developed for layered heterostructures, but large-scale growth control over wet-chemical synthesis with comprehensive efficiency remains elusive. Here we demonstrate an interfacial coassembly heterobilayer films from perylene and graphene oxide (GO) precursors, which are spontaneously formed at the interface, with uniform bilayer structure of single-crystal perylene and well-stacked GO over centimeters in size. The planar heterostructure device exhibits an ultrahigh specific detectivity of 3.1 × 10 13 Jones and ultralow energy consumption of 10 −9  W as well as broadband photoperception from 365 to 1550 nm. Moreover, the device shows outstanding photonic synaptic behaviors with a paired-pulse facilitation (PPF) index of 214% in neuroplasticity, the heterosynapse array has the capability of information reinforcement learning and recognition. Layered heterostructures are promising photosensitive materials for advanced optoelectronics. Here, the authors introduce an interfacial coassembly method to construct large-scale perylene/grahene oxide (GO) heterobilayer for broadband photoreception and efficient neuromorphics.

Electrochemical synthesis of H 2 O 2 through a selective two-electron (2e − ) oxygen reduction reaction (ORR) is an attractive alternative to the industrial anthraquinone oxidation method, as it allows decentralized H 2 O 2 production. Herein, we report that the synergistic interaction between partially oxidized palladium (Pd δ+ ) and oxygen-functionalized carbon can promote 2e − ORR in acidic electrolytes. An electrocatalyst synthesized by solution deposition of amorphous Pd δ+ clusters (Pd 3 δ+ and Pd 4 δ+ ) onto mildly oxidized carbon nanotubes (Pd δ+ -OCNT) shows nearly 100% selectivity toward H 2 O 2 and a positive shift of ORR onset potential by ~320 mV compared with the OCNT substrate. A high mass activity (1.946 A mg −1 at 0.45 V) of Pd δ+ -OCNT is achieved. Extended X-ray absorption fine structure characterization and density functional theory calculations suggest that the interaction between Pd clusters and the nearby oxygen-containing functional groups is key for the high selectivity and activity for 2e − ORR. Electrocatalysts which deliver high selectivity for two-electron oxygen reduction to hydrogen peroxide in acidic media are sought to replace the conventional anthraquinone process. Here, the authors develop partially oxidised palladium clusters on carbon nanotubes with near 100% selectivity.

Autophagy is an evolutionarily conserved dynamic process and present in variety of cells at basal levels to maintain homeostasis and to promote cell survival in response to stresses. The early bone loss with excessive glucocorticoids (GCs) was reported to be related with the extension of the life span of osteoclasts. However, the connection between GCs induced bone loss and osteoclast autophagy remains to be elucidated. Autophagy was detected in a Dexamethasone (Dex) induced osteoporotic mice model and primary osteoclast cultures by autophagosome detection kit, and autophagy-related proteins were assayed by Western blotting and Immunostaining. The bone morphology was examined by micro-CT and TRAP staining. The trabecular bone micro-architecture was deteriorated, and the osteoclast number and spread area were increased in the Dex-treated mice compared with the control group ( P  < 0.01). Meanwhile, autophagy in pre-osteoclasts was increased in mice under Dex administration evidenced by the increased number of autophagosome and up-regulation of autophagy-related protein levels. Further, the enhanced autophagy under Dex treatment was verified in primary cultured osteoclasts, as shown by the increased levels of Beclin 1 and LC3-II/LC3-I and the autophagy complex formation members including Atg1, Atg13, and Atg7. However, the expressions of PI3K, p-Akt and p-mTOR in primary cultured osteoclasts were inhibited under Dex induced autophagy. Using the selective PTEN inhibitor SF1670 to activate the PI3K/Akt/mTOR pathway reversed this osteoclast autophagy under Dex treatment. Our study suggests that osteoclast autophagy was enhanced in glucocorticoids induced bone loss, and the PI3K/Akt/mTOR signaling pathway mediated the increased autophagy in primary cultured osteoclasts under glucocorticoids treatment.

Accumulating vast amounts of pollutants drives modern civilization toward sustainable development. Construction waste is one of the prominent issues impeding progress toward net-zero. Pollutants must be utilized in constructing civil engineering structures for a green ecosystem. On the other hand, large-scale production of industrial steel fibers (ISFs) causes significant damage to the goal of a sustainable environment. Recycled steel fibers (RSFs) from waste tires have been suggested to replace ISFs. This research critically examines RSF’s application in the mechanical properties’ improvement of concrete and mortar. A statistical analysis of dimensional parameters of RSFs, their properties, and their use in manufacturing various cement-based composites are given. Furthermore, comparative assessments are carried out among the improvements in compressive, split tensile, and flexural strengths of plain and RSF-incorporated concrete and mortar. In addition, the optimum contents of RSF for each strength property are also discussed. The influence of RSFs parameters on various strength properties of concrete and mortars is discussed. The possible applications of RSF for various civil engineering structures are reviewed. The limitations and errors noticed in previous review papers are also outlined. It is found that the maximum enhancement in compressive strength (CS), split tensile strength (STS), and flexure strength (FS) are 78%, 149%, and 157%, respectively, with the addition of RSF into concrete. RSF increased cement mortars’ CS, STS, and FS by 46%, 50.6%, and 69%, respectively. The current study encourages the building sector to use RSFs for sustainable concrete.

Introduction Radiopharmaceuticals that target cancer-associated fibroblasts (CAFs) have become an increasingly attractive strategy for cancer theranostics. Recently, a series of fibroblast activation protein inhibitor (FAPI)-based radiopharmaceuticals have been successfully applied to the diagnosis of a variety of cancers and exhibited excellent tumor selectivity. Nevertheless, CAF-targeted radionuclide therapy encounters difficulties in cancer treatment, as the tumor uptake and retention of FAPIs are insufficient. To meet this challenge, we tried to conjugate albumin-binding moiety to FAPI molecule for prolonged circulation that may increase the accumulation and retention of radiopharmaceuticals in tumor. Methods Two fatty acids, lauric acid (C12) and palmitic acid (C16), were conjugated to FAPI-04 to give two albumin-binding FAPI radiopharmaceuticals, denoted as FAPI-C12 and FAPI-C16, respectively. They had been radiolabeled with gallium-68, yttrium-86, and lutecium-177 for stability study, binding affinity assay, PET and SPECT imaging, biodistribution, and radionuclide therapy study to systematically evaluate their potential for CAF-targeted radionuclide therapy. Results FAPI-C12 and FAPI-C16 showed high binding affinity to FAP with the IC 50 of 6.80 ± 0.58 nM and 5.06 ± 0.69 nM, respectively. They were stable in both saline and plasma. The tumor uptake of [ 68 Ga]Ga-FAPI-04 decreased by 56.9% until 30 h after treated with FAPI-C16 before, and the uptakes of [ 86 Y]Y-FAPI-C12 and [ 86 Y]Y-FAPI-C16 in HT-1080-FAP tumor were both much higher than that of HT-1080-Vehicle tumor which identified the high FAP specific of these two radiopharmaceuticals. Both FAPI-C12 and FAPI-C16 showed notably longer circulation and significantly enhanced tumor uptake than those of FAPI-04. [ 177 Lu]Lu-FAPI-C16 had the higher tumor uptake at both 24 h (11.22 ± 1.18%IA/g) and 72 h (6.50 ± 1.19%IA/g) than that of [ 177 Lu]Lu-FAPI-C12 (24 h, 7.54 ± 0.97%IA/g; 72 h, 2.62 ± 0.65%IA/g); both of them were much higher than [ 177 Lu]Lu-FAPI-04 with the value of 1.24 ± 0.54%IA/g at 24 h after injection. Significant tumor volume inhibition of [ 177 Lu]Lu-FAPI-C16 at the high activity of 29.6 MBq was observed, and the median survival was 28 days which was much longer than that of the [ 177 Lu]Lu-FAPI-04 treated group of which the median survival was only 10 days. Conclusion This proof-of-concept study validates the hypothesis that conjugation of albumin binders may shift the pharmacokinetics and enhance the tumor uptake of FAPI-based radiopharmaceuticals. This could be a general strategy to transform the diagnostic FAP-targeted radiopharmaceuticals into their therapeutic pairs.

Background Common walnut ( Juglans regia L.) is one of the top four most consumed nuts in the world due to its health benefits and pleasant taste. Despite its economic importance, the evolutionary history and genetic control of its adaptation and agronomic traits remain largely unexplored. Results We report a comprehensive walnut genomic variation map based on whole-genome resequencing of 815 walnut accessions. Evolutionary analyses suggest that Chinese J. regia diverged from J. sigillata with extensive hybridizations after the split of the two species. In contrast to annual crops, the genetic diversity and heterozygous deleterious mutations of Chinese common walnut trees have continued to increase during the improvement process. Selective sweep analyses identify 902 genes uniquely selected in the improved common walnut compared to its progenitor population. Five major-effect loci are identified to be involved in walnut adaptations to temperature, precipitation, and altitude. Genome-wide association studies reveal 27 genomic loci responsible for 18 important agronomic traits, among which JrFAD2 and JrANR are the potentially major-effect causative genes controlling linoleic acid content and color of the endopleura of the nut, respectively. Conclusions The largest genomic resource for walnuts to date has been generated and explored in this study, unveiling their evolutionary history and cracking the genetic code for agronomic traits and environmental adaptation of this economically crucial crop tree.

Hydrogels, comprising 3D hydrophilic polymer networks, have emerged as promising biomaterial candidates for emulating the structure of biological tissues and delivering drugs through topical administration with good biocompatibility. Nanozymes can catalyze endogenous biomolecules, thereby initiating or inhibiting biological processes. A nanozyme-hydrogel composite inherits the biological functions of hydrogels and nanozymes, where the nanozyme serves as the catalytic core and the hydrogel forms the structural scaffold. Moreover, the composite can concentrate nanozymes in targeted lesions and catalyze the binding of a specific group of substrates, resulting in pathological microenvironment remodeling and drug-penetrating barrier impairment. The composite also shields nanozymes to prevent burst release during catalytic production and reduce related toxicity. Currently, the application of these composites has been extended to antibacterial, anti-inflammatory, anticancer, and tissue repair applications. In this review, we elucidate the preparation methods for nanozyme-hydrogel composites, provide compelling evidence of their advantages in drug delivery and provide a comprehensive overview of their biological application.

This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.

Background: Inflammation and immune dysfunction play significant roles in the pathogenesis of Alzheimer's disease (AD)-related dementia. Changes in peripheral blood cell profiles are a common manifestation of inflammation and immune dysfunction and have been reported in patients with AD or mild cognitive impairment (MCI). We systematically evaluated the association of peripheral blood cell counts and indices with AD or MCI through a meta-analysis. Methods: We electronically searched sources to identify all case‒control trials comparing peripheral blood cell counts and/or lymphocyte subsets between patients with AD or MCI and healthy controls (HCs). Meta-analyses were used to estimate the between-group standardized mean difference (SMD) and 95% confidence interval (CI). Results: A total of 36 studies involving 2,339 AD patients, 608 MCI patients, and 8,352 HCs were included. AD patients had significantly decreased lymphocyte counts (SMD -0.345, 95% CI [-0.545, -0.146], P = 0.001) and significantly increased leukocyte counts (0.140 [0.039, 0.241], P = 0.006), neutrophil counts (0.309 [0.185, 0.434], P = 0.01), and neutrophil‒lymphocyte ratio (NLR) (0.644 [0.310, 0.978], P < 0.001) compared to HCs. Similarly, significantly increased leukocyte counts (0.392 [0.206, 0.579], P < 0.001), NLR (0.579 [0.310, 0.847], P < 0.001), and neutrophil counts (0.248 [0.121, 0.376], P < 0.001) were found in MCI patients compared with HCs. A significantly decreased percentage of B lymphocytes (-1.511 [-2.775, -0.248], P = 0.019) and CD8+ T cells (-0.760 [-1.460, -0.061], P = 0.033) and a significantly increased CD4/CD8 ratio (0.615 [0.074, 1.156], P = 0.026) were observed in AD patients compared to HCs. Furthermore, significant changes in hemoglobin level and platelet distribution width were found in patients with AD or MCI compared with HCs. However, no significant difference was found between AD or MCI patients and HCs in latelet counts, mean corpuscular volume, red cell distribution width, mean platelet volume, and CD4+ T, CD3+ T, or natural killer cell counts. Conclusion: Changes in peripheral blood cell profiles, particularly involving leukocyte, lymphocyte, neutrophil, and CD8+ T cell counts, as well as the NLR and the CD4/CD8 ratio, are closely associated with AD. The diagnostic relevance of these profiles should be investigated in future.