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Magnetically actuated momentum-driven millirobots
A smart nail platform for wireless subsoil health monitoring via unmanned aerial vehicle-assisted radio frequency interrogation
FGF7 promotes load-bearing tendon regeneration and suppresses fibrosis
Abstract Tissue fibrosis is a major cause of organ dysfunction. Preventing fibrosis in tissue pathological condition remains a significant clinical challenge. Here we investigate the role of fibroblast growth factor 7 (FGF7) in mitigating fibrosis and promoting regeneration of load-bearing tendons. Fgf7 knockout mice exhibit impaired motor function and disordered matrix assembly in tendons. Single-cell RNA sequencing reveals an enrichment of a pro-fibrotic cell subpopulation in Fgf7-deficient tendons, which is also predominant in human tendinopathy. Using ProTracer technology, we find that FGF7 deficiency drives proliferating cells toward this pro-fibrotic lineage. Furthermore, we find that FGF7 promotes the tenogenic differentiation of tendon stem/progenitor cells while suppressing their fibrotic differentiation. Importantly, a hydrogel loaded with recombinant FGF7 effectively reduces fibrosis and significantly promotes functional tendon regeneration in vivo. These findings elucidate FGF7’s dual role in driving tenogenesis and inhibiting fibrosis, suggesting a potential therapeutic strategy.
High-fat diet disrupts a septal control on feeding to promote obesity in male mice
Interactions with bacteria shape diatom adaptation to carbon concentration changes
Abstract Diatoms are key contributors to global primary production, and have developed intricate partnerships with bacteria through long-term co-evolution. Here, we uncover a syntrophic relationship between the model obligate photoautotroph diatom Phaeodactylum tricornutum and the rod-shaped bacterium Loktanella vestfoldensis , which enables the diatom to indirectly utilize glucose. To be specific, growth of the diatom depends on the support of L. vestfoldensis for the supply of necessary carbon source when glucose serves as the sole carbon source, while L. vestfoldensis shows dependence on P. tricornutum when CO 2 is the sole carbon source. Reanalysis of Tara Oceans metagenomic data shows frequent co-occurrence of Loktanella with diatoms including Chaetoceros and Thalassiosira , indicating the ecological relevance of this partnership. Co-culture with L. vestfoldensis supports robust growth of Chaetoceros muelleri and Thalassiosira pseudonana in the presence of glucose as the sole carbon source. Transcriptomic and metabolomic analyses reveal that P. tricornutum maintains a photoautotrophic metabolism in co-culture, as indicated by the up-regulation of genes involved in inorganic carbon concentration and photosynthesis, while the co-cultured bacterium likely supplies CO 2 and growth-stimulating metabolites such as indole-3-acetic acid. Our findings demonstrate that bacterial-algal interactions may shape diatom adaptation to carbon changes and contribute to marine carbon cycling.
A triboelectric-piezoelectric-electromagnetic self-powerd wearable sensor for long-term human rehabilitation training monitoring
Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN
Abstract Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.26% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier’s performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split the data, which not only aids in reducing overfitting but also helps in generalizing the model.
Prediction of regolith particle scattering considering adhesive force during spacecraft landing under low gravity
Fresh and strength properties of high volume ultra-fine fly ash cement mortar with calcinated limestone powder
Abstract The growing demand for sustainable building materials has led to an increased quest for substitute cementitious systems that can mitigate environmental impact while maintaining the characteristics of traditional cementitious systems. Ordinary Portland cement (OPC) production contributes significantly to CO₂ emissions, necessitating the development of sustainable alternatives. Fly ash, being an industrial by-product, is used with cement to make high-volume fly ash blended (HVFA) cement, which offers substantial environmental and economic benefits by limiting the use of ordinary Portland cement. Yet, its extensive usage is hindered by performance downsides such as slower early-age strength and deferred setting time. This study aims to develop a sustainable mortar system by integrating multiple industrial by-products, including ultra-fine fly ash (UFFA), calcinated limestone powder (CLP), and bauxite powder (BP), activated with alkaline activators (AA). The primary objective is to replace 40–60% of ordinary Portland cement, thereby improving the mechanical and durability properties of the mortar compared to conventional mortar systems. In the production of sustainable mortar, incorporating 40–60% of ultra-fine fly ash, 10–20% of calcinated limestone powder, 0–10% of bauxite powder, and 3–9 g of alkaline activator. Mortar specimens were prepared with varying proportions of industrial by-products: 40–60% ultra-fine fly ash, 10–20% calcinated limestone powder, 0–10% bauxite powder, and 3–9 g of alkaline activator, maintaining a 1:3 binder-to-fine aggregate ratio. Comprehensive testing was implemented, including workability assessment, setting time determination, compressive strength evaluation, flexural strength testing, fracture toughness analysis, and detailed microstructural characterisation. The optimum mixture composition, consisting of 40% ordinary Portland cement, 40% ultra-fine fly ash, 15% low thermal calcinated lime powder, and 5% bauxite powder with alkaline activator made from 6 g of magnesium carbonate powder, demonstrated superior performance characteristics. This optimal blend achieved enhanced mechanical properties compared to conventional mortar while successfully replacing 60% of ordinary Portland cement with industrial by-products. The improved performance of the optimum mixture can be attributed to synergistic effects between ultra-fine fly ash, calcinated limestone powder, and bauxite powder under alkaline activation. Future research should investigate the long-term durability performance of the developed sustainable mortar under various environmental exposure conditions.
Red-billed blue magpie optimization for training feedforward neural networks
Understanding water properties in tumorous murine cells using field-cycling NMR relaxometry
Abstract The fixed high magnetic fields (1–7 Tesla) used for magnetic resonance imaging produce resolution suitable for oncology but image contrast is insufficient to determine tumour stage. Fast field cycling nuclear magnetic resonance (FFC NMR) measurements spanning low fields (0.24 mT–0.24 T) provide frequency-dependent longitudinal relaxation rate $$R_1(f)$$ profiles which allow healthy and pathological tissue to be differentiated. In vivo FFC NMR measurements from healthy and tumorous murine tissue spanning a range of tumour fractions have been interpreted using the 3-Tau model (3TM). Each 3TM fit yields six physically meaningful fit parameters. Statistically significant correlation with tumour fraction is found for two of these parameters, namely the surface-to-volume ratio and bulk water dynamic time constant. These fit parameters therefore act as biomarkers. The sensitivity of the biomarkers to tumour fraction is explained by the net water ingress into tumour cells. The sensitivity of $$R_1(f)$$ at the lowest field ( $$f=0.01$$ MHz) is explained by changes in the surface-to-volume ratio. $$R_1(f=0.01)$$ is also a biomarker. The water dynamics at solid surfaces are found to change with tumour fraction probably due to differences in cell wall structure between healthy and pathological tissue. FFC NMR measurements interpreted with the 3TM have the potential to estimate tumour fraction from biopsy samples from humans.
Study of CO2 capture by synthesized composite and modelling with machine learning and response surface methodology
Comparing low-field cryogenic nuclear relaxation of hyperpolarized diamond and silicon particles
Abstract We report on field cycling experiments with hyperpolarized diamond and silicon particles between 10 mT and 3.4 T at temperatures below 10 K. Diamonds with approximately 54 ppm defects, of which around 58% were P1 centers, were hyperpolarized by continuous-wave dynamic nuclear polarization (DNP) at 3.4 T. For fields above 200 mT, the 13 C relaxation in diamond was measured to be nearly independent of the magnetic field. At around 200 mT, the field dependence changed and $$T_1$$ was approximately proportional to the field strength. For example, the relaxation time decreased approximately threefold by reducing the main magnetic field from 200 mT to 75 mT. The 13 C relaxation was measured to be independent of the DNP polarization time and nuclear hyperpolarization levels. In contrast, the relaxation of hyperpolarized silicon was found to be independent of the field strength down to a few mT, despite a relatively short time for DNP build-up. The results suggest that magnetic fields greater than approximately 200 mT are required for hyperpolarized diamonds with several ppm of (nitrogen) defects to ensure sufficiently long relaxation times.
RETRACTED ARTICLE: Real-time monitoring system for early stroke detection based on fog computing and enhanced deep learning techniques
A fetal autopsy study on congenital malformations among stillbirths in a tertiary care hospital
Anticancer potential of Dendrocnide meyeniana through phytochemical profiling, ADMET analysis, molecular docking, and in silico cytotoxicity evaluation
Abstract Phytochemicals are widely explored for cancer therapeutics due to their structural diversity and broad pharmacological activities. This study investigated the phytochemical composition and anticancer potential of Dendrocnide meyeniana using integrated in silico approaches. Gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) identified 78 compounds, confirming the plant’s rich chemical diversity. Four cancer-related targets- EGFR, p53, MMP7 and CDK8/Cyclin C were selected for molecular docking to identify potential inhibitors. Drug-likeness and ADMET profiling of nine bioactive candidates revealed Cryptotanshinone as the most promising compound, exhibiting favorable pharmacokinetic and safety properties. Molecular docking showed that Cryptotanshinone possessed strong binding affinities toward EGFR ( -8.8 kcal/mol), p53 (-8.7 kcal/mol), MMP7 (-8.7 kcal/mol), and CDK8/Cyclin C (-9.8 kcal/mol), comparable to or exceeding the reference drug Erlotinib (-9.0 kcal/mol for EGFR). Toxicity prediction indicated no hepatotoxic, mutagenic, or cytotoxic effects, though the compound showed potential carcinogenic activity possibly linked to pathway-specific interaction in cell-cycle regulation. Molecular dynamics simulation further validated the stability of the Cryptotanshinone–EGFR complex, exhibiting moderate RMSD values and limited structural fluctuations indicative of stable interactions. Collectively, these findings highlight Cryptotanshinone from D. meyeniana as a promising natural lead for anticancer drug development, characterized by strong binding affinity, favorable pharmacokinetics, and structural stability in silico. Further in vitro and in vivo studies are warranted to confirm its therapeutic efficacy and safety.
Synergistic adsorption and oxidation of arsenite by Fe–Mn binary oxide-modified bamboo biochar in the presence of air
Abstract The adsorption and oxidation processes of arsenite (As(III)) are investigated in this work utilizing bamboo biochar (BC) modified with Fe, Mn, and Fe-Mn binary oxides (FBC, MBC, FMBC). The modification resulted in substantial enhancements to the bamboo biochar’s surface area, pore structure, and functional groups. Batch adsorption experiments showed that FMBC achieved the highest As(III) removal capacity, reaching 5.86 mg g − 1 , outperforming both FBC and MBC. The kinetics data were fitted with a pseudo second-order model and analyzed using the Langmuir-Freundlich isotherm model, resulting in a good correlation coefficient. Characterization techniques, including XRD, FTIR, XPS, and BET analysis, confirmed successful loading of metal oxides and demonstrated As(III) oxidation to arsenate (As(V)) during adsorption, particularly under oxic conditions. Manganese oxides played a pivotal role in catalyzing the oxidation of As(III), whereas iron oxides predominantly facilitated the adsorption of arsenic (As) species. pH and coexisting anions were found to influence As removal, with FMBC maintaining high performance across a broad pH range. Additionally, the presence of dissolved oxygen (DO) enhanced As(III) oxidation, improving overall removal efficiency. The synergistic action of the Fe-Mn oxides significantly enhanced both the rate and extent of As(III) oxidation, a critical step for subsequent adsorption. These combined features position FMBC as an efficient, cost-effective, and highly promising functionalized biochar for As removal.