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Mechanochromic cholesteric liquid crystal devices for mechanical strain detection
Abstract The properties of functional soft materials have opened the doors for the development of a wide range of stimuli-responsive smart devices. The ability to react to factors such as humidity, temperature or pressure, inducing changes in the materials’ properties, for instance, colour, constitutes the basis of smart sensing. For such applications, liquid crystals (LCs) present a particularly compelling and innovative class of materials that have been extensively studied. Amongst LCs, cholesteric LCs (CLCs) have been gaining much attention for different applications, owing to their selective and tuneable reflection of circularly polarised light. This work was focused on the application of CLC-based smart sensing devices for mechanical strain detection through the production of mechanical stress-sensitive devices. These sensors may be used, for instance, as standalone sensors to evaluate a crack propagation in concrete structures, having a visual readout. For this effect, CLC elastomer (CLCE)-based mechanochromic devices were produced. PDMS was embedded with 3D CLCE structures, obtained through the crosslinking of an acrylate-terminated CLCE (ACLCE) precursor solution, followed by shaping either by dropwise addition onto a silicon oil bath – with and without agitation – or by moulding. The sensorial capabilities of the devices were tested by subjecting them to mechanical deformation to assess whether this leads to a change in colour. It was concluded that, considering the main goal of achieving mechanochromic behaviour, the ACLCE structures obtained from dropwise addition, under no agitation, exhibited the strongest and most uniform mechanochromism.
Exploring anatomical similarity in zero-shot learning for bone abnormality detection
Alterations in peripheral blood B cell subsets and their clinical significance in systemic lupus erythematosus
Abstract Systemic lupus erythematosus (SLE) is characterized by B-cell dysregulation and autoantibody production. This study aimed to characterize the alterations in peripheral blood B-cell subsets across different states of SLE and to evaluate their diagnostic value and association with specific autoantibodies.A total of 84 participants were enrolled, comprising 64 SLE patients (including 20 newly diagnosed, 27 stable, and 17 with lupus nephritis) and 20 healthy controls (HCs). Peripheral blood B-cell subsets, including naïve B-cells, memory B-cells, and plasmablasts, were analyzed by flow cytometry. Their diagnostic performance was assessed using Receiver Operating Characteristic (ROC) curve analysis. Associations with anti-nuclear antibody (ANA), anti-dsDNA, and anti-Sm antibody status were also evaluated.Compared to HCs, SLE patients exhibited significant disturbances in B-cell homeostasis. The most consistent finding was a profound decrease in the absolute frequency of memory B cells across all patient groups (newly diagnosed, stable, and LN; all p < 0.001). Newly diagnosed patients showed a significant expansion of plasmablasts (p = 0.017), which was less pronounced in stable and LN groups. ROC analysis demonstrated that the absolute memory B-cell frequency (memory B%) had outstanding diagnostic performance for SLE (AUC = 0.905, 80% sensitivity, 80% specificity). Furthermore, higher anti-nuclear antibody (ANA) titers and anti-dsDNA positivity were significantly associated with a decreased absolute naïve B-cell count and an increased relative proportion of plasmablasts. Anti-Sm positivity was specifically linked to a higher plasmablast proportion (p = 0.006).Our findings highlight a marked disruption of peripheral B-cell subsets in SLE, particularly a persistent reduction in memory B cells and an expansion of plasmablasts in active disease. Memory B cells serve as an excellent diagnostic biomarker, while plasmablast expansion is closely associated with specific autoantibodies, underscoring the pivotal role of aberrant B-cell differentiation in SLE immunopathogenesis.
Tailoring Ba3PCl3-based perovskite solar cells via multi-parameter optimization for high power conversion efficiency
Safety and efficacy of privacy-preserving models to create Lay summaries of brain MRI reports
Green synthesis of activated carbon-ZIF-8 nanocomposites from pistachio hulls for efficient antibiotic adsorption in water remediation
Combining gait analysis and finite element modeling to optimize offloading insoles for calcaneal ulcers
Multi-scenario simulation of land use change and landscape ecological vulnerability analysis in Fuzhou City based on GA-PLUS coupled modeling
The glymphatic system clears amyloid beta and tau from brain to plasma in humans
Molecular identification and optimization of indole acetic acid production by Fusarium oxysporum AUMC 16,438 for biofertilizer application
Abstract Indole-3-acetic acid (IAA) is a key phytohormone that regulates essential plant physiological processes and enhances overall growth. This study aimed to produce and optimize IAA synthesis by a fungal isolate obtained from rhizosphere soils in Egypt. Twenty fungal isolates were screened for IAA production, and isolate FSA12 showed the highest yield (23.98 ± 1.98 µg/mL). Molecular identification based on ITS sequencing, supported by morphological characterization, revealed that FSA12 is most likely Fusarium oxysporum AUMC 16,438, and its sequence was deposited in GenBank (accession no. PP990199). Optimization using the OFAT approach indicated that the optimal L-tryptophan concentration, incubation temperature, pH, incubation period, and inoculum size were 0.6%, 30 °C, pH 6, 12 days, and three mycelial discs, respectively. To reduce production cost, several agro-industrial wastes were evaluated as alternative carbon sources; banana peel proved the most effective, likely due to its natural L-tryptophan content. Application of the produced IAA significantly enhanced wheat seed germination compared with the untreated control, demonstrating its potential use as an eco-friendly biofertilizer in sustainable agriculture.
The frequency response of networks as open systems
Abstract Many biological, technological, and social systems can be effectively described as networks of interacting subsystems. Typically, these networks are not isolated objects, but interact with their environment through both signals and information that are received by specific nodes with an input function or released to the environment by other nodes with an output function. An important question is whether the structure of different networks, together with the particular selection of input and output nodes, is such that it favors the passing or blocking of such signals. For a given network and a given choice of the input and output nodes, the $${{{{\mathcal{H}}}}}_{2}$$ H 2 -norm provides a natural and general quantification of the extent to which input signals–whether deterministic or stochastic, periodic or arbitrary–are amplified. We analyze a diverse set of empirical networks and find that many naturally occurring systems, such as food webs, signaling pathways, and gene regulatory circuits, are structurally organized to enhance the passing of signals; in contrast, the structure of engineered systems like power grids appears to be intentionally designed to suppress signal propagation.
Analysis of the impact of vertical deep hole blasting at the bottom of the hole on the lower ore body based on LS-dyna numerical simulation
Safeguarding climate-resilient mangroves requires only a moderate increase in the global protected area
Abstract Climate change and anthropogenic activities threaten biodiversity and ecosystem services. Climate-smart conservation plans address these challenges by ensuring protection of some climate-resilient areas. However, integrating climate change in the design of conservation plans is often deemed too expensive, as it may require larger networks or protecting more costly sites from a conservation perspective. Using mangroves as a case study, we evaluate the efficiency of protecting mangroves in climate-smart versus climate-naïve reserve networks. We find that climate-smart conservation plans could provide sizable benefits (13.3%) for relatively moderate increases in protected area (+7.3%). Moreover, transboundary plans, involving cooperation among countries, require less area and protect more climate-resilient mangroves than nation-by-nation plans. Implementing these strategies would improve the current protected area network for mangroves, which currently has poor climate resilience. Our methodology could potentially be tested on other ecosystems, assuming sufficient information exists regarding their distribution, biodiversity, and resilience to climate change.
Generalized blood vessel models for magnetic nanoparticle-based oncology: geometric and microfluidic properties
Abstract Superparamagnetic iron oxide nanoparticles (SPIONs) represent an emerging class of nanoparticles that face increasing applications in medicine, in particular in nanoparticle-based oncology. Their superparamagnetic properties allow for the magnetic steering and the interlinked targeted and localized delivery of pharmaceuticals. The development of nanoparticle-based therapies requires a deep understanding of geometry-hydrodynamics-adhesion interactions, motivating the generation of blood vessel models. The present work addresses the geometry-dependent propagation of SPIONs under magnetic steering through generalized, transferable geometries. Such geometries were derived based on generalized, statistical considerations of branch-dependent vessel diameters, yielding a reproducible and transferable testing environment independent of individual angiographic data. Based on stereolithographic additive manufacturing, fluidic models with varied blood vessel diameters and branching orders were manufactured and tested under varying magnetic steering conditions, injected SPION concentration, and flow rate. Through optical in situ measurements and complementary ex situ scanning electron microscopy, a significant influence of flow-regime-dependent hydrodynamic effects on magnetic steerability could be identified. Experimental findings suggest that while reduced flow rates were associated with locally laminar flows that promoted sedimentation and enabled limited magnetic redistribution of SPION-containing colloidal solutions at a magnetic flux density of B = 0.35 T, increased flow rates and interlinked unsteady flows were shown to impair the magnetically controlled, local SPION deposition. Hence, flow conditions present in larger arteries and bifurcations during SPION injection can be shown to significantly influence the quantitative magnetic steering of SPIONs in vascular-inspired fluidic channel structures, displaying enhanced local particle residence times and redistribution of SPIONs under low-flow conditions.
The functional landscape of alternative splicing in hematopoietic lineage commitment
Abstract Alternative splicing (AS) is a ubiquitous post-transcriptional regulatory mechanism, that has greatly expanded the transcriptomic and proteomic diversity in vertebrates. While gene regulation of hematopoiesis has been extensively researched in vertebrates, the functions of species- and cell lineage-specific splice variants in vertebrates are largely unknown. Here, we curate transcriptomic data on fetal hematopoietic organ development in six vertebrates and hematopoietic cell differentiation in humans and mice. To identify functional exon-skipping events among thousands of cassette exons in protein-coding genes for a specific differentiation lineage and species, we develop a machine-learning model interrogating 19 features including dynamic expression, protein structure, and evolutionary conservation, and integrate them into a single prediction score, named Functional AS Score (FAScore). Using FAScore, we identify four previously-uncharacterized functional AS events in which deletion of the AS exon leads to defects in erythropoiesis and myelopoiesis. Furthermore, we demonstrate that deletion of exon 15 of TBC1D23 reduces erythropoiesis in mice and zebrafish through elevated binding capacity to RANBP2/RANGAP1 leading to increased SUMOylation level of HDAC1 . Collectively, our study presents a valuable tool to identify functional exon skipping (ES) events during hematopoietic lineage commitment, and establishes a research paradigm that can be broadly applied to other biological processes.
Functional characterization of a type I-F1 CRISPR-cas system from the clinical isolate Shewanella xiamenensis Sh95 reveals constitutive activity and plasmid-curing capability
CCMIM: Optimizing concrete defect detection through state-space modeling and dynamic feature fusion
Concrete defect detection is crucial to the safety, reliability, and durability of structures. For CNN models, it is impossible to obtain all information at different scales and complex backgrounds, nor can it capture all contexts globally. Transformer-based models are computationally intensive, making it difficult to generalize to real-time detection tasks. To address these issues, we propose a novel end-to-end concrete crack detection framework: Concrete Crack Mamba-in-Mamba (CCMIM). Specifically, we introduce the Mamba-In-Mamba (MiM) module to capture long-range dependencies and global context to improve the concrete defect detection capability based on hierarchical data flow. In addition, this paper also proposes the Dynamic Dual Fusion (DDF) module, which enhances the robustness and adaptability of the model and achieves smooth multi-scale fusion by dynamically changing the feature representation. To reduce the computational cost and maintain spatial information, we propose the Sparse Pyramid Transformer (SPT) module. This module reduces the computation and improves the inference speed by selecting tokens level by level (from coarse to fine) and sharing attention parameters, but does not sacrifice accuracy. Experimental results show that the CCMIM model outperforms traditional methods as well as YOLO- and Transformer-based models in small crack detection across multiple datasets. Specifically, on the RDD2022, SDNET2018, and CCCD datasets, the accuracy reached 89.2%, 85.2%, and 79.3%, respectively, while the mAP50 reached 88.1%, 87.8%, and 79.2%. In summary, the CCMIM model provides an effective solution for concrete defect detection. The code can be accessed at: https://github.com/lixiaozhen01/CCMIM .
Large-scale capsid-mediated mobilisation of bacterial genomic DNA in the gut microbiome
Abstract Transducing bacteriophage and gene transfer agents (GTAs) are constrained by the structural limits of their capsids, which determine the maximum length of host DNA they can package. Here, we utilise nanopore sequencing of intact, capsid-packaged DNA molecules to recover full-length reads, thereby enabling the precise identification of encapsidated DNA and its bacterial origin. This approach was validated using well-characterised transducing systems and subsequently applied to faecal viromes from three healthy donors. Our analysis reveals that bacterial DNA encapsidation is widespread in the gut microbiome, with up to 5.4% of capsid-packaged DNA derived from bacterial genomes. Generalised transduction and GTA activity were especially prominent in Oscillospiraceae and Ruminococcaceae (e.g. Faecalibacterium spp.), while lateral transduction was observed in Bacteroides . Additionally, we detected induction of prophages in several highly prevalent gut bacterial taxa. These findings reveal the prevalence of bacterial DNA packaging via virus or virus-like capsids in the human gut, shedding light on the diverse mechanisms that drive this process.