Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials.

Numerical solutions to partial differential equations (PDEs) are instrumental for material structural design where extensive data screening is needed. However, traditional numerical methods demand significant computational resources, highlighting the need for innovative optimization algorithms to streamline design exploration. Direct gradient-based optimization algorithms, while effective, rely on design initialization and require complex, problem-specific sensitivity derivations. The advent of machine learning offers a promising alternative to handling large parameter spaces. To further mitigate data dependency, researchers have developed physics-informed neural networks (PINNs) to learn directly from PDEs. However, the intrinsic continuity requirement of PINNs restricts their application in structural mechanics problems, especially for composite materials. Our work addresses this discontinuity issue by substituting the PDE residual with a weak formulation in the physics-informed training process. The proposed approach is exemplified in modeling digital materials, which are mathematical representations of complex composites that possess extreme structural discontinuity. This article also introduces an interactive process that integrates physics-informed loss with design objectives, eliminating the need for pretrained surrogate models or analytical sensitivity derivations. The results demonstrate that our approach can preserve the physical accuracy in data-free material surrogate modeling but also accelerates the direct optimization process without model pretraining.

Physics-informed shape optimization using coordinate projection.

The rapid growth of artificial intelligence is revolutionizing classical engineering society, offering novel approaches to material and structural design and analysis. Among various scientific machine learning techniques, physics-informed neural network (PINN) has been one of the most researched subjects, for its ability to incorporate physics prior knowledge into model training. However, the intrinsic continuity requirement of PINN demands the adoption of domain decomposition when multiple materials with distinct properties exist. This greatly complicates the gradient computation of design features, restricting the application of PINN to structural shape optimization. To address this, we present a novel framework that employs neural network coordinate projection for shape optimization within PINN. This technique allows for direct mapping from a standard shape to its optimal counterpart, optimizing the design objective without the need for traditional transition functions or the definition of intermediate material properties. Our method demonstrates a high degree of adaptability, allowing the incorporation of diverse constraints and objectives directly as training penalties. The proposed approach is tested on magnetostatic problems for iron core shape optimization, a scenario typically plagued by the high permeability contrast between materials. Validation with finite-element analysis confirms the accuracy and efficiency of our approach. The results highlight the framework's capability as a viable tool for shape optimization in complex material design tasks.

Anatomy and Comparative Transcriptome Reveal the Mechanism of Male Sterility in Salvia miltiorrhiza.

Salvia miltiorrhiza Bunge is an important traditional herb. Salvia miltiorrhiza is distributed in the Sichuan province of China (here called SC). Under natural conditions, it does not bear seeds and its sterility mechanism is still unclear. Through artificial cross, there was defective pistil and partial pollen abortion in these plants. Electron microscopy results showed that the defective pollen wall was caused by delayed degradation of the tapetum. Due to the lack of starch and organelle, the abortive pollen grains showed shrinkage. RNA-seq was performed to explore the molecular mechanisms of pollen abortion. KEGG enrichment analysis suggested that the pathways of phytohormone, starch, lipid, pectin, and phenylpropanoid affected the fertility of S. miltiorrhiza. Moreover, some differentially expressed genes involved in starch synthesis and plant hormone signaling were identified. These results contribute to the molecular mechanism of pollen sterility and provide a more theoretical foundation for molecular-assisted breeding.

Deep Learning Accelerated Design of Mechanically Efficient Architected Materials.

Lattice structures are known to have high performance-to-weight ratios because of their highly efficient material distribution in a given volume. However, their inherently large void fraction leads to low mechanical properties compared to the base material, high anisotropy, and brittleness. Most works to date have focused on modifying the spatial arrangement of beam elements to overcome these limitations, but only simple beam geometries are adopted due to the infinitely large design space associated with probing and varying beam shapes. Herein, we present an approach to enhance the elastic modulus, strength, and toughness of lattice structures with minimal tradeoffs by optimizing the shape of beam elements for a suite of lattice structures. A generative deep learning-based approach is employed, which leverages the fast inference of neural networks to accelerate the optimization process. Our optimized lattice structures possess superior stiffness (+59%), strength (+49%), toughness (+106%), and isotropy (+645%) compared to benchmark lattices consisting of cylindrical beams. We fabricate our lattice designs using additive manufacturing to validate the optimization approach; experimental and simulation results show good agreement. Remarkable improvement in mechanical properties is shown to be the effect of distributed stress fields and deformation modes subject to beam shape and lattice type.

Monitoring Anomalies in 3D Bioprinting with Deep Neural Networks.

Additive manufacturing technologies have progressed in the past decades, especially when used to print biofunctional structures such as scaffolds and vessels with living cells for tissue engineering applications. Part quality and reliability are essential to maintaining the biocompatibility and structural integrity needed for engineered tissue constructs. As a result, it is critical to detect for any anomalies that may occur in the 3D-bioprinting process that can cause a mismatch between the desired designs and printed shapes. However, challenges exist in detecting the imperfections within oftentimes transparent bioprinted and complex printing features accurately and efficiently. In this study, an anomaly detection system based on layer-by-layer sensor images and machine learning algorithms is developed to distinguish and classify imperfections for transparent hydrogel-based bioprinted materials. High anomaly detection accuracy is obtained by utilizing convolutional neural network methods as well as advanced image processing and augmentation techniques on extracted small image patches. Along with the prediction of various anomalies, the category of infill pattern and location information on the image patches can be accurately determined. It is envisioned that using our detection system to categorize and localize printing anomalies, real-time autonomous correction of process parameters can be realized to achieve high-quality tissue constructs in 3D-bioprinting processes.

Generative machine learning algorithm for lattice structures with superior mechanical properties.

Lattice structures are typically made up of a crisscross pattern of beam elements, allowing engineers to distribute material in a more structurally effective way. However, a main challenge in the design of lattice structures is a trade-off between the density and mechanical properties. Current studies have often assumed the cross-sectional area of the beam elements to be uniform for reducing the design complexity. This simplified approach limits the possibility of finding superior designs with optimized weight-to-performance ratios. Here, the optimized shape of the beam elements is investigated using a deep learning approach with high-order Bézier curves to explore the augmented design space. This is then combined with a hybrid neural network and genetic optimization (NN-GO) adaptive method for the generation of superior lattice structures. In our optimized design, the distribution of material is smartly shifted more towards the joint region, the weakest location of lattice structures, to achieve the highest modulus and strength. This design strikes to balance between two modes of deformation: axial and bending. Thus, the optimized design is efficient for load bearing and energy absorption. To validate our simulations, the optimized design is then fabricated by additive manufacturing and its mechanical properties are evaluated through compression testing. A good correlation between experiments and simulations is observed and the optimized design has outperformed benchmark ones in terms of modulus and strength. We show that the extra design flexibility from high-order Bézier curves allows for a smoother transition between the beam elements which reduces the overall stress concentration profile.

Ultrasonic-Assisted Aqueous Two-Phase Extraction Combined with Macroporous Resin Enrichment of Lignans from Flaxseed Meal and Their Antioxidant Activities.

BACKGROUND: Secoisolariciresinol diglucoside (SDG) is a natural antioxidant generally extracted from flaxseed, which is one of the most important oil crops in China, the by-product of the flaxseed oil, i.e., flaxseed meal still contains a lot of lignans. However, flaxseed meal is generally treated as waste, resulting in a huge waste of resources. OBJECTIVE: To establish an efficient and convenient method for extraction and purification of lignans from flaxseed meal. METHODS: First, we used response surface methodology (RSM) to optimize the extraction conditions of the ultrasonic-assisted (UA) aqueous two-phase system (ATPS), and we obtained the purified extracts by macroporous resin purification (MRP). Second, the antioxidant ability of the extracts was studied in vitro. RESULTS: The best extraction conditions obtained were as follows: 9.0% (w/w) sodium hydroxide; 30.0% (w/w) isopropanol; extraction time, 39 min; liquid-to-solid ratio, 52.0 mL/g; ultrasonic wave, 560 W; and extraction temperature, 40°C. Under the optimal conditions, the purity of crude extracts (SDG-APTS-C) reached 21.5%. The desorption conditions of MRP were as follows: eluting 3 BV with ultrapure water, and then eluting with 25% ethanol at 2 BV (bed volume)/h to collect eluents. The purified extracts (SDG-ATPS-P) had a purity quotient of 73.9%, which was 52.4% higher than that of SDG-ATPS-C. Additionally, experiments conducted revealed that SDG-ATPS-C and SDG-ATPS-P could effectively remove DPPH (2,2-Diphenyl-1-picrylhydrazyl), ABTS （2,-Azinobis-3-ethylbenzthiazoline-6-sulphonate）, and hydroxyl free radicals in vitro. CONCLUSIONS: The method was validated for extracting SDG from flaxseed meal, thus achieving the reuse of flaxseed meal. HIGHLIGHTS: This research provides some references for the application of UAATPS combined with MRP in natural products.

Ultrasensitive colorimetric strategy for Hg2+ detection based on T-Hg2+-T configuration and target recycling amplification.

A novelty aptasensor for ultrasensitive detection of Hg2+ is developed, exploiting the combination of plasmonic properties of gold nanoparticles (AuNPs) and exonuclease III (Exo III)-assisted target recycling for signal amplification. In the presence of Hg2+, a DNA duplex can be formed due to the strong coordination of Hg2+ and T bases of single-stranded DNA (ssDNA) probe. Exo III digests the DNA duplex from the 3' to 5' direction, resulting in the releasing of Hg2+. Then, the released Hg2+ binds with another ssDNA probe through T-Hg2+-T coordination. After Exo III-assisted Hg2+ cycles, numerous ssDNA probes are exhausted, which promotes poly(diallyldimethylammonium chloride) (PDDA)-induced AuNP aggregation, leading to an obvious color change and aggregation-induced plasmon red shift of AuNPs (from 520 to 610 nm). Therefore, this biosensor is ultrasensitive, which is applicable to the detection of trace level of Hg2+ with a linear range from 5 pM to 0.6 nM and an ultralow detection limit of 0.2 pM. Furthermore, it enables visual detection of Hg2+ as low as 50 pM by the naked eye. More importantly, the assay can be applied to the reliable determination of spiked Hg2+ in sea water samples with good recovery.

Bacterial Behavior in Confined Spaces.

In confined spaces, bacteria exhibit unexpected cellular behaviors that are related to the biogeochemical cycle and human health. Types of confined spaces include lipid vesicles, polymer vesicles, emulsion droplets, microfluidic chips, and various laboratory-made chambers. This mini-review summarizes the behaviors of living bacteria in these confined spaces, including (a) growth and proliferation, (b) cell communication, and (c) motion. Future trends and challenges are also discussed in this paper.

Highly sensitive and selective detection and intracellular imaging of glutathione using MnO2 nanosheets assisted enhanced fluorescence of gold nanoclusters.

Glutathione (GSH) plays a critical role in biological defense system and is associated with numerous human pathologies. However, it still remains a challenge for fluorescent detection of GSH over cysteine (Cys) and homocysteine (Hcy) because of their similar structures. In this work, MnO2 nanosheets can efficiently quench the fluorescence of gold nanoclusters (Met-AuNCs) prepared by blending methionine and HAuCl4 owing to their superior absorption capability. However, GSH can reduce MnO2 nanosheets into Mn2+ which leads to the fluorescence recovery of Met-AuNCs. More intriguingly, GSH can dramatically and selectively enhance the fluorescence intensity of Met-AuNCs. Hence, a low background, ultrasensitive fluorescent detection of GSH was obtained with a detection limit of 68 nM. Moreover, the assay has been successfully used for GSH detection in human serum samples and cellular imaging with high selectivity over Cys and Hcy.

Birefringent Silk Fibroin Hydrogel Constructed via Binary Solvent-Exchange-Induced Self-Assembly.

Birefringent hydrogels have a strong potential for applications in biomedicine and optics as they can modulate the optical and mechanical anisotropy in confined two-dimensional geometries. However, production of birefringent hydrogels with hierarchical structures, mechanical properties, and biorelated behavior that are analogous to biological tissues is still challenging. Starting from the silk fibroin (SF)-ionic liquid solution system, this study aimed to rationally design a "binary solvent-exchange-induced self-assembly (BSEISA)" strategy to produce birefringent SF hydrogels (SFHs). In this method, the conformational transition rate of SF can be effectively controlled by the exchange rate of the binary solvents. Therefore, this method provides the possibility of controlling the conformation and orientation of SF. Molecular simulations confirmed that methanol is more effective in driving ß-sheet formation than other often used solvents, such as formic acid and water. The formed ß-sheets act as the physical cross-links that connect disparate protein chains, thereby forming continuous and stable three-dimensional (3D) hydrogel networks. The resultant BSEISA-SFHs are transparent and birefringent with mechanical characteristics similar to those of soft biological tissues, such as lens and cartilage. Interestingly, our results revealed that the evolution of experimental birefringent fringes perfectly matched the changes in stress distribution predicted using finite element analysis. Owing to the unique birefringence of BSEISA-SFHs, together with the advantages in mechanical performance, these hydrogels are anticipated to act as good tissue surrogates for understanding the mechanical response of biological tissues.

Colorimetric determination of DNA using an aptamer and plasmonic nanoplatform.

A facile plasmonic nanoplatform was developed for rapid and sensitive determination of nucleic acid. Hg2+-regulated molecular beacon (MB, hairpin) containing rich thymine (T) bases at both ends is used as the probe. A hairpin structure can be formed in the MB probe due to the strong binding of Hg2+ to T. However, in the presence of target DNA, the hairpin structure is opened owing to target DNA-specific hybridization with the aptamer. Simultaneously, the opened MB interacts with poly(diallyldimethylammonium chloride) (PDDA) and hinders PDDA-induced aggregation of AuNPs, accompanied by a color change from blue to red and a decrease in absorption ratio (A620/A520). Hence, a good linear relationship was observed between the decreased absorption ratio (A620/A520) and DNA concentration ranging from 0.02 to 2 nmol/L with a low detection limit of 4.42 pmol/L. Moreover, this nanoplatform has been successfully utilized to discriminate between perfect target and mismatch sequences. More importantly, the bioassay is simple, versatile, rapid, and cost-effective compared with other common methods, which holds great promise for clinical diagnosis and biomedical application. Graphical abstract.

Design, synthesis and biological evaluation of novel N-sulfonylamidine-based derivatives as c-Met inhibitors via Cu-catalyzed three-component reaction.

In our continuing efforts to develop novel c-Met inhibitors as potential anticancer candidates, a series of new N-sulfonylamidine derivatives were designed, synthesized via Cu-catalyzed multicomponent reaction (MCR) as the key step, and evaluated for their in vitro biological activities against c-Met kinase and four cancer cell lines (A549, HT-29, MKN-45 and MDA-MB-231). Most of the target compounds showed moderate to significant potency at both the enzyme-based and cell-based assay and possessed selectivity for A549 and HT-29 cancer cell lines. The preliminary SAR studies demonstrated that compound 26af (c-Met IC50 = 2.89 nM) was the most promising compound compared with the positive foretinib, which exhibited the remarkable antiproliferative activities, with IC50 values ranging from 0.28 to 0.72 µM. Mechanistic studies of 26af showed the anticancer activity was closely related to the blocking phosphorylation of c-Met, leading to cell cycle arresting at G2/M phase and apoptosis of A549 cells by a concentration-dependent manner. The promising compound 26af was further identified as a relatively selective inhibitor of c-Met kinase, which also possessed an acceptable safety profile and favorable pharmacokinetic properties in BALB/c mouse. The favorable drug-likeness of 26af suggested that N-sulfonylamidines may be used as a promising scaffold for antitumor drug development. Additionally, the docking study and molecular dynamics simulations of 26af revealed a common mode of interaction with the binding site of c-Met. These positive results indicated that compound 26af is a potential anti-cancer candidate for clinical trials, and deserves further development as a selective c-Met inhibitor.

Chemical Information Exchange in Organized Protocells and Natural Cell Assemblies with Controllable Spatial Positions.

An ultrasound-based platform is established to prepare homogenous arrays of giant unilamellar vesicles (GUVs) or red blood cell (RBCs), or hybrid assemblies of GUV/RBCs. Due to different responses to the modulation of the acoustic standing wave pressure field between the GUVs and RBCs, various types of protocell/natural cell hybrid assemblies are prepared with the ability to undergo reversible dynamic reconfigurations from vertical to horizontal alignments, or from 1D to 2D arrangements. A two-step enzymatic cascade reaction between transmitter glucose oxidase-containing GUVs and peroxidase-active receiver RBCs is used to implement chemical signal transduction in the different hybrid micro-arrays. Taken together, the obtained results suggest that the ultrasound-based micro-array technology can be used as an alternative platform to explore chemical communication pathways between protocells and natural cells, providing new opportunities for bottom-up synthetic biology.

High-throughput sequencing analysis of marine pioneer surface-biofilm bacteria communities on different PDMS-based coatings.

Marine biofilms, the attachment of marine microorganisms on artificial surfaces in natural seawater, play critical roles in the development of marine biofouling, which pave ways for the settlement and colonization of sessile invertebrate larvae. Despite the excellent microbe-inhibitory effect of polydimethylsiloxane (PDMS)-based coatings, marine bacteria could still attach to surfaces and form natural biofilms. However, there is little information available on the common structural features of pioneer surface-biofilm bacteria (PSB) communities on different PDMS-based coatings with regard to their compositions, distributions and diversity. Herein, the present study aims to explore the compositional and structural features of the PSB communities on different PDMS-based coatings using 16S rRNA gene amplicon sequencing in terms of the taxonomic structures at phylum, family and genus level. The results revealed the PSB communities on different PDMS-based coatings possessed high similarities in compositional, structural and diversity features, but varied greatly in relative abundance and distributions. Proteobacteria was the most diverse and overwhelming phylum in biofilms formed on all PDMS-based coatings, followed by Cyanobacteria. In addition, the decreased abundance of Proteobacteria and the increased abundance of Cyanobacteria on the carbon nanotubes (CNTs)-modifed PDMS composites (CPCs) may contribute to their differential anti-biofouling effect against the colonization of juvenile macrofoulers.

Differential Colonization Dynamics of Marine Biofilm-Forming Eukaryotic Microbes on Different Protective Coating Materials.

In this study, the actual anti-biofouling (AF) efficacy of three protective coatings, including a chlorinated rubber-based coating (C0) and two polydimethylsiloxane (PDMS)-based coatings (P0 and PF), were estimated via the static field exposure assays. The surface properties of these protective coatings, including surface wettability and morphology features, were characterized using the static water contact angle (WCA) and scanning electron microscope (SEM). The colonization and succession dynamics of the early-adherent biofilm-forming eukaryotic microbial communities occupied on these protective coatings were explored using the Single-stranded Conformation Polymorphism (SSCP) technique. The field data clearly revealed that coating P0 and PF performed better in the long-term static submergence, as compared with the C0 surface, while coating PF showed excellent AF efficacy in the field. Fingerprinting analysis suggested that the diversity, abundance, the clustering patterns, and colonization dynamics of the early-colonized eukaryotic microbes were significantly perturbed by these protective coatings, particularly by the PF surfaces. These differential AF efficacy and perturbation effects would be largely ascribed to the differences in the wettability and surface nanostructures between the C0, P0 and PF surfaces, as evidenced by WCA and SEM analysis.

A label-free hairpin aptamer probe for colorimetric detection of adenosine triphosphate based on the anti-aggregation of gold nanoparticles.

A facile and rapid colorimetric approach was described for selective and sensitive determination of adenosine triphosphate (ATP) based on a hairpin aptamer probe and the anti-aggregation of AuNPs. Poly(diallyldimethylammonium chloride) (PDDA) can induce the aggregation of AuNPs due to the electrostatic interaction causing a red to blue color change. Upon the addition of ATP, aptamer-based hairpin probe is opened and releases flexible ssDNA ends. The released flexible ssDNA ends can interact with PDDA and prevent PDDA-induced AuNPs aggregation. Thus, a visible color change from blue to red and a decrease in the absorption ratio (A610/A520) are observed. Under the optimal conditions, the hairpin aptamer-based colorimetric assay exhibits high sensibility and selectivity for the detection of ATP with a detection limit of 1.7nM. Moreover, this assay is successfully used in the rapid determination of ATP in spiked human serum samples with good recoveries in the range of 102.88 to 104.07%.

A hairpin-type DNA probe for direct colorimetric detection of endonuclease activity and inhibition based on the deaggregation of gold nanoparticles.

A method is described for the determination of the activity of endonuclease. It based on the deaggregation of gold nanoparticles (AuNPs) aggregated by the action of poly(diallyldimethylammonium chloride) (PDDA). A single-stranded DNA (ssDNA) is released after enzymatic cleavage catalyzed by endonuclease. The released fragments bind electrostatically to PDDA and inhibit the PDDA-induced aggregation of AuNPs. This is accompanied by a color change from blue to red and a decrease in the absorption ratio (A630/A520). Under the optimal conditions, this ratio increases linearly in the 0.001 to 1 U·µL-1 EcoRI endonuclease activity range. The detection limit is of 2 × 10-4 U·µL-1 which is much better or at least comparable to previous reports. The method is deemed to have wide scope in that it may be used to study other endonuclease activity (such as BamHI) by simply changing the specific recognition site of the hairpin-like DNA probe. The assay may also be employed to screening for inhibitors of EcoRI endonuclease. Graphical abstract Schematic presentation of the colorimetric assay based on the deaggregation of AuNPs for the detection of endonuclease activity. A single-stranded sequence (ssDNA) is released by the EcoRI cleavage, which electrostatically binds to PDDA and inhibits the PDDA-induced aggregation of AuNPs accompanying with a color change from blue to red.

Age-dependent effects of floxuridine (FUdR) on senescent pathology and mortality in the nematode Caenorhabditis elegans.

Floxuridine (5'-fluorodeoxyuridine, FUdR) acts as an inhibitor of DNA replication by binding to thymidylate synthase and is widely used to treat colorectal cancer. FUdR is also frequently used in research on aging in C. elegans since by blocking reproduction it allows maintenance of synchronous nematode populations. Here we examine age-specific effects of exposure to 50 µM FUdR on pathology and mortality. We report that initiating exposure to FUdR at late development or early adulthood reduces lifespan but later initiation increases it. Moreover, earlier initiation leads to enhancement of senescent intestinal atrophy, but amelioration of several other senescent pathologies (pharyngeal degeneration and uterine tumors). These results provide further evidence of the complex effects of FUdR on aging in C. elegans, and therefore support the argue against its routine use in studies of nematode aging due to its possible confounding effects. However, they also illustrate how effects of FUdR on aging are interesting in their own right.