Granular Support Research Articles

4D Bioprinting Shape‐Morphing Tissues in Granular Support Hydrogels: Sculpting Structure and Guiding Maturation

AbstractDuring embryogenesis, organs undergo dynamic shape transformations that sculpt their final shape, composition, and function. Despite this, current organ bioprinting approaches typically employ bioinks that restrict cell‐generated morphogenetic behaviors resulting in structurally static tissues. This work introduces a novel platform that enables the bioprinting of tissues that undergo programmable and predictable 4D shape‐morphing driven by cell‐generated forces. This method utilizes embedded bioprinting to deposit collagen‐hyaluronic acid bioinks within yield‐stress granular support hydrogels that can accommodate and regulate 4D shape‐morphing through their viscoelastic properties. Importantly, precise control over 4D shape‐morphing is possible by modulating factors such as the initial print geometry, cell phenotype, bioink composition, and support hydrogel viscoelasticity. Further, shape‐morphing is found to actively sculpt cell and extracellular matrix alignment along the principal tissue axis through a stress‐avoidance mechanism. To enable predictive design of 4D shape‐morphing patterns, a finite element model is developed that accurately captures shape evolution at both the cellular and tissue levels. Finally, it is demonstrated that programmed 4D shape‐morphing enhances the structural and functional properties of iPSC‐derived heart tissues. This ability to design, predict, and program 4D shape‐morphing holds great potential for engineering organ rudiments that recapitulate morphogenetic processes to sculpt their final shape, composition, and function.

On the Relation between the Viscoelastic Properties of Granular Hydrogels and Their Performance as Support Materials in Embedded Bioprinting.

Granular hydrogels, formed by jamming microgels suspension, are promising materials for three-dimensional bioprinting applications. Despite their extensive use as support materials for embedded bioprinting, the influence of the particle's physical properties on the macroscale viscoelasticity on one hand and on the printing performance on the other hand remains unclear. Herein, we investigate the linear and nonlinear rheology of κ-carrageenan granular hydrogel through small- and large-amplitude oscillatory shear measurements. We tuned the granular hydrogel's properties by changing the stiffness (soft, medium, stiff) and the packing density of the individual microgels. Characterizations in the linear viscoelasticity regime revealed that the storage modulus of granular hydrogels is not a simple function of microgel stiffness and depends on the microgel packing density. At larger strains, increasing the microgel stiffness reduced the energy dissipation of the granular beds and increased the solid-fluid transition point. To understand how the different rheological properties of granular support materials influence embedded bioprinting, we examined the printing fidelity and cellular filament shrinkage within the granular beds. We found that microgels with low packing density diminished the printing quality, while stiff microgels promoted filament roughness. In addition, we found that highly packed stiff microgels significantly reduced the postprinting contraction of cellular filaments. Overall, this work provides a comprehensive knowledge of the rheology of granular hydrogels that can be used to rationally design support beds for bioprinting applications with specific characteristics.

Synthesis and characterization of new generation of sorbent materials and their application in the elimination of diclofenac: Numerical analysis using Comsol software

This paper reports the preparation of a new class of eco- friendly sorbents as well as the study of the continuous dynamic sorption of diclofenac (DF) chosen as a model representative of pharmaceutical pollutants. Biomass based on palm waste was used as base material in the presence of 27% commercial polymer carbopol as granular support, 10% tween 80 as surfactant and NaOH in the preparation of hydrophobic and organophilic effective sorbent grains. Multiple experiments were conducted to investigate the impact of initial diclofenac concentration (20–50 mg/L), flow rate (1.11–1.82 L/h), and column height (10–15 cm) on the breakthrough curve. The maximum adsorption capacity was found to be 137 mg/g under a flow rate of 1.11 L/h, column height 15 cm and inlet concentration of diclofenac of 20 mg/L. The experimental data obtained data were fitted using the empirical models of Thomas, Yoon - Nelson and Adam-Bohart. The reusability assessment demonstrated the potential for reusing this active biomass for a minimum of three adsorption –desorption cycles. The breakthrough curves indicated a positive correlation between breakthrough time and column height, while initial diclofenac concentration and flow rate exhibited negative correlations. The utilization of the Thomas model effectively described the sorption process, supported by a highly satisfactory correlation coefficient (R² > 0.98). However, a marginal decrease in the sorption capacity of diclofenac was observed with the number of sorption-desorption cycles, with reductions of 24%, 10%, and 5%, respectively.

Numerical Design of Granular Support for Three-Way Catalyzed Solid- and Porous-Particles Membrane Filters.

A granular substrate used as a support for a three-way catalyzed (TWC) solid-particle membrane filter was investigated through numerical simulation. The proposed support could reduce the amount of required catalyst material by 39% and lower the pressure drop by 33%, compared to a conventional filter, while achieving almost 100% soot-filtration. Moreover, TWC porous particles, which are designed to introduce a fluid flow into their interconnected pore network, further decrease the pressure drop. However, a trade-off exists between the amount of the introduced fluid flow and the specific surface area.

Granular Hydrogels in Biofabrication: Recent Advances and Future Perspectives.

Granular hydrogels, which are formed by densely packing microgels, are promising materials for bioprinting due to their extrudability, porosity, and modularity. However, the multidimensional parameter space involved in granular hydrogel design makes material optimization challenging. For example, design inputs such as microgel morphology, packing density, or stiffness can influence multiple rheological properties that govern printability and the behavior of encapsulated cells. This review provides an overview of fabrication methods for granular hydrogels, and then examines how important design inputs can influence material properties associated with printability and cellular responses across multiple scales. Recent applications of granular design principles in bioink engineering are described, including the development of granular support hydrogels for embedded printing. Further, the paper provides an overview of how key physical properties of granular hydrogels can influence cellular responses, highlighting the advantages of granular materials for promoting cell and tissue maturation after the printing process. Finally, potential future directions for advancing the design of granular hydrogels for bioprinting are discussed.

Microgel-Extracellular Matrix Composite Support for the Embedded 3D Printing of Human Neural Constructs.

The embedded 3D printing of cells inside a granular support medium has emerged in the past decade as a powerful approach for the freeform biofabrication of soft tissue constructs. However, granular gel formulations have been restricted to a limited number of biomaterials that allow for the cost-effective generation of large amounts of hydrogel microparticles. Therefore, granular gel support media have generally lacked the cell-adhesive and cell-instructive functions found in the native extracellular matrix (ECM). To address this, a methodology has been developed for the generation of self-healing annealable particle-extracellular matrix (SHAPE) composites. SHAPE composites consist of a granular phase (microgels) and a continuous phase (viscous ECM solution) that, together, allow for both programmable high-fidelity printing and an adjustable biofunctional extracellular environment. This work describes how the developed methodology can be utilized for the precise biofabrication of human neural constructs. First, alginate microparticles, which serve as the granular component in the SHAPE composites, are fabricated and combined with a collagen-based continuous component. Then, human neural stem cells are printed inside the support material, followed by the annealing of the support. The printed constructs can be maintained for weeks to allow the differentiation of the printed cells into neurons. Simultaneously, the collagen continuous phase allows for axonal outgrowth and the interconnection of regions. Finally, this works provides information on how to perform live-cell fluorescence imaging and immunocytochemistry to characterize the 3D-printed human neural constructs.

Comparative analysis of the residues of granular support bath materials on printed structures in embedded extrusion printing

Embedded extrusion printing facilitates the fabrication of complex biological structures using soft hydrogels that are challenging to construct using conventional manufacturing methods. While this targeting strategy is appealing, the residues of support materials on the printed objects have been overlooked. Here, we quantitatively compare the bath residues on fibrin gel fibers printed in granular gel baths that are conjugated with fluorescent probes for visualization, including physically crosslinked gellan gum (GG) and gelatin (GEL) baths and chemically crosslinked polyvinyl alcohol baths. Notably, all support materials can be detected on a microscopic scale, even on structures without any visible residues. Quantitative results indicate that baths with smaller size or lower shear viscosity show more and deeper diffusion into the extruded inks, and the removal efficiency of support materials depends mainly on the dissolving property of the granular gel baths. The residual amount of chemically cross-linked support materials on fibrin gel fibers is 28–70 μg mm−2, which is tens of times higher than physically cross-linked GG (7.5 μg mm−2) and GEL (0.3 μg mm−2) baths. Meanwhile, cross-sectional images suggest that most gel particles are distributed around the fiber surface, but a small amount is in the fiber center. Such bath residues or the blank pores created by the removal of gel particles induce changes in product surface morphology, physicochemical and mechanical properties, impeding cell adhesion. This study will draw attention to the effects of residual support materials on printed structures and encourage the development of new strategies to diminish these residues or to take advantage of the residual support baths to improve product performances.

A qualitative process evaluation using the behaviour change wheel approach: Did a whole genome sequence report form (SRF) used to reduce nosocomial SARS-CoV-2 within UK hospitals operate as anticipated?

The aim of this study was to conduct a process evaluation of a whole-genome sequence report form (SRF) used to reduce nosocomial SARS-CoV-2 through changing infection prevention and control (IPC) behaviours within the COVID-19 pandemic. We used a three-staged design. Firstly, we described and theorized the purported content of the SRF using the behaviour change wheel (BCW). Secondly, we used inductive thematic analysis of one-to-one interviews (n = 39) to explore contextual accounts of using the SRF. Thirdly, further deductive analysis gauged support for the intervention working as earlier anticipated. It was possible to theorize the SRF using the BCW approach and visualize it within a simple logic model. Inductive thematic analyses identified the SRF's acceptability, ease of use and perceived effectiveness. However, major challenges to embedding it in routine practice during the unfolding COVID-19 crisis were reported. Notwithstanding this insight, deductive analysis showed support for the putative intervention functions 'Education', 'Persuasion' and 'Enablement'; behaviour change techniques '1.2 Problem solving', '2.6 Biofeedback', '2.7 Feedback on outcomes of behaviour' and '7.1 Prompts and cues'; and theoretical domains framework domains 'Knowledge' and 'Behavioural regulation'. Our process evaluation of the SRF, using the BCW approach to describe and theorize its content, provided granular support for the SRF working to change IPC behaviours as anticipated. However, our complementary inductive thematic analysis highlighted the importance of the local context in constraining its routine use. For SRFs to reach their full potential in reducing nosocomial infections, further implementation research is needed.

Cobalt–aluminum oxide clusters-embedded γ-Al2O3 nanosheets for peroxymonosulfate activation: Interfacial pH-buffering property to eliminate cobalt leaching and boost the catalytic activity

Cobalt–aluminum oxide clusters-embedded γ-Al2O3 nanosheets (CAO@AO) were facilely fabricated and developed for peroxymonosulfate (PMS) activation. Interestingly, the Al–OH-rich amphiphilic surface of CAO@AO endowed the catalyst with unique interfacial pH-buffering property, which stabilized the reaction media at nearly neural level and thus prevented the cobalt sites from leaching. Moreover, the presence of Al–OH groups around the Co−Al oxide clusters facilitated the activation of PMS and the redox cycle of Co2+/Co3+, which resulted in a boosted catalytic activity. Consequently, the CAO@AO exhibited zero leaching of cobalt and high efficacy for PMS activation, enabling complete and ultrafast removal of metronidazole with a significantly higher turnover frequency (TOF, 1.83 min−1) than that of Co3O4 (0.004 min−1). It was also demonstrated that introducing suitable foreign metal into CAO@AO could further enhance the catalytic activity (e.g., TOF of Mn(40%)-CAO@AO: 5.80 min−1). Furthermore, Mn(40%)-CAO@AO-decorated granular support was synthesized that dominated a continuous-flow degradation in column reaction system.

"Out-of-the-box" Granular Gel Bath Based on Cationic Polyvinyl Alcohol Microgels for Embedded Extrusion Printing.

Embedded extrusion printing provides a versatile platform for fabricating complex hydrogel-based biological structures with living cells. However, the time-consuming process and rigorous storage conditions of current support baths hinder their commercial application. This work reports a novel "out-of-the-box" granular support bath based on chemically crosslinked cationic polyvinyl alcohol (PVA) microgels, which is ready to use by simply dispersing the lyophilized bath in water. Notably, with ionic modification, PVA microgels yield reduced particle size, uniform distribution, and appropriate rheological properties, contributing to high-resolution printing. Following by the lyophilization and re-dispersion process, ion-modified PVA baths recover to its original state, with unchanged particle size, rheological properties, and printing resolution, demonstrating its stability and recoverability. Lyophilization facilitates the long-term storage and delivery of granular gel baths, and enables the application of "out-of-the-box" support materials, which will greatly simplify experimental procedures, avoid labor-intensive and time-consuming operations, thus accelerating the broad commercial development of embedded bioprinting.

Print-and-Grow within a Novel Support Material for 3D Bioprinting and Post-Printing Tissue Growth.

3D bioprinting holds great promise for tissue engineering, with extrusion bioprinting in suspended hydrogels becoming the leading bioprinting technique in recent years. In this method, living cells are incorporated within bioinks, extruded layer by layer into a granular support material followed by gelation of the bioink through diverse cross-linking mechanisms. This approach offers high fidelity and precise fabrication of complex structures mimicking living tissue properties. However, the transition of cell mass mixed with the bioink into functional native-like tissue requires post-printing cultivation in vitro. An often-overlooked drawback of 3D bioprinting is the nonuniform shrinkage and deformation of printed constructs during the post-printing tissue maturation period, leading to highly variable engineered constructs with unpredictable size and shape. This limitation poses a challenge for the technology to meet applicative requirements. A novel technology of "print-and-grow," involving 3D bioprinting and subsequent cultivation in κ-Carrageenan-based microgels (CarGrow) for days is presented. CarGrow enhances the long-term structural stability of the printed objects by providing mechanical support. Moreover, this technology provides a possibility for live imaging to monitor tissue maturation. The "print-and-grow" method demonstrates accurate bioprinting with high tissue viability and functionality while preserving the construct's shape and size.

Carbon scaffold with controllable small pore structure based on mask projection stereolithography

Mask Projection Stereolithography (MPSL), due to its high printing resolution, is suitable for fabricating porous polymer architectures that are used as carbon scaffold precursors. In the present work, a carbon scaffold with a controllable small porous structure was fabricated by MPSL. A polymer lattice structure used as the carbon scaffold precursor was first fabricated by MPSL. Sodium chloride was selected as the granular support, and nano-silica particles were introduced into the granular support so that sodium chloride could easily fill the lattice structure. A carbon scaffold with controllable low shrinkage (41.3%) and a small porous structure (skeleton thickness=~40 µm and pore size=~300 µm) was obtained by pyrolyzing the polymer lattice structure in the granular support, and subsequently, it was processed by hydrofluoric acid (HF). The unprocessed and HF-processed scaffolds were both composed of simple substance carbon, and silicon oxide particles embedded on the unprocessed scaffold were cleaned by HF. Finally, the compressive properties of the scaffold were examined. The fracture appearances of the scaffold under the application of compressive forces parallelly and vertically to the stacking direction were different, and compressive properties in vertical to the stacking direction were superior to those in parallel to the stacking direction. Therefore, it can be inferred that the proposed method is effective in fabricating multi-scale carbon scaffolds with small controllable porous structures based on stress analysis to obtain optimum mechanical properties, which makes it a promising material for many applications.

Crop Yield Prediction using Granular SVM

Agriculture is the backbone of the Indian economy. Farming is a major source of income for many people in developing countries. Prediction of yield of crops is desirable as it can predict the income and minimise losses for the farmersunder unfavorable conditions. But predicting crop yield is a challenging task in developing countries like India. Conventionally, crop yield prediction is done using farmer’s expertise. The sustainability and productivity of a crop growing area are dependent on suitable climatic, soil, and biological conditions. So, data mining techniques based on neural networks, Neuro-Fuzzy Inference Systems, Fuzzy Logic, SMO, and Multi Linear Regression can be used for prediction. Previous work has performed yield prediction based on crop models considering only some of the environmental factors. This work uses a Support Vector Machine (SVM) to predict the crop yield under different environmental conditions that include soil, climate, and biological factors. Applying granular computing enables dividing the problem space into a sequence of subtasks. So, the hyperplane construction of SVM can be parallelized by splitting the problem space. Testing can also be parallelized. The main advantage is that linear SVM can be used to handle higher dimension space. Time complexity is reduced. Prediction using granular SVM can be parallelized using appropriate techniques like MapReduce/GPGPU. IoT-based agriculture increases crop yield by accurate prediction, automation, remote monitoring, and reducing wastage of resources. IoT-based monitoring systems can be used by farmers, researchers, and government officials to analyze crop environments and statistical information to predict crop yield. This paper proposes an IoT-based system to predict crop yield based on climatic, soil, and biological factors using parallelized granular support vector machines.

FunEffector-Pred: Identification of Fungi Effector by Activate Learning and Genetic Algorithm Sampling of Imbalanced Data

Fungal pathogens have evolved the ability to cause serious plant diseases and threaten the world food security. Fungal effectors are proteins that exploit the host cellular functions to facilitate infection. Effector identification is crucial for disease control in crops and to understand plant-pathogen interactions. However, fungal effector identification has been challenging as most fungal effectors lack of consensus motifs and data imbalance problem. In this study, a fungal effector predictor was designed to effectively learn from an imbalanced dataset. A granular support vector-based under-sampling (GSV-US) strategy combined with a genetic algorithm was used for majority class sampling. When evaluating on an independent test dataset, the FunEffector-Pred significantly outperformed the existing predictors for fungal effector identification. Several informative feature patterns, such as the patterns of Ile, Gly, Val, Leu and Thr, as well as the combination of aromatic amino acids with positively-charged amino acids, are reported for fungal effector identification for the first time.

Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites.

The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors. In this study, we aim to relieve the negative impact of class imbalance by Boosting Multiple Granular Support Vector Machines (BGSVM). In BGSVM, each base SVM is trained on a granular training subset consisting of all minority samples and some reasonably selected majority samples. The efficacy of BGSVM for dealing with class imbalance was validated by benchmarking it with several typical imbalance learning algorithms. We further implemented a protein-nucleotide binding site predictor, called BGSVM-NUC, with the BGSVM algorithm. Rigorous cross-validation and independent validation tests for five types of proteinnucleotide interactions demonstrated that the proposed BGSVM-NUC achieves promising prediction performance and outperforms several popular sequence-based protein-nucleotide binding site predictors. The BGSVM-NUC web server is freely available at http://csbio.njust.edu.cn/bioinf/BGSVM-NUC/ for academic use.

STICKER: An Energy-Efficient Multi-Sparsity Compatible Accelerator for Convolutional Neural Networks in 65-nm CMOS

STICKER is an energy-efficient convolutional neural network (NN) processor. It mainly improves energy efficiency by making full use of sparsity. The network sparsity can potentially lower storage and computation requirements. However, the sparsity distribution of both activations and weights ranges from 2% to 99% in different layers or models. Therefore, good support for the sparsity distribution is the key to improve the energy efficiency. Three new features are proposed in this article to support wide sparsity distribution efficiently. First, multi-sparsity control and data flow are implemented for finer sparsity granularity support. It can automatically switch the processor among nine sparsity modes for higher energy efficiency. Second, a multi-mode hierarchical data memory which can be reconfigured for networks with different sparsity modes is designed for higher storage efficiency. Third, a multi-sparsity-compatible set-associative convolution processing element (PE) array is designed to efficiently carry out convolution operations under different sparsity modes, especially when both activations and weights are sparse. STICKER was implemented in a 65-nm CMOS technology. With its wide-range sparsity-supported capacity, the peak energy efficiency reaches 62.1 TOPS/W when sparsity ratios of both activations and weights are 5%. In a completely pruned Alexnet model, STICKER achieves 2.82 TOPS/W energy efficiency 1.8 $\times $ higher than that of the state-of-the-art processors.

Understanding the Allocation of Student Support Personnel in Public Schools

In this study, we extend research on the allocation of educational resources to an underdeveloped domain—the distribution of counselors, psychologists, and social workers (i.e., support personnel) to schools. Using administrative data for all North Carolina public schools in the 2007–2008 through 2015–2016 school years, we measure the allocation of support personnel to schools, track secular changes in support personnel ratios during a period in which state budgetary provisions contracted and expanded, and assess the distribution of support personnel to high-poverty and high-minority schools. We calculate more accurate support personnel ratios and show that trends in support personnel ratios differ across school levels. We find that districts concentrate support personnel in high-need schools, however, this compensatory pattern is narrowing over time. Our findings call for further research on the allocation of support personnel and charge states/districts to calculate more accurate and granular support personnel ratios to inform decision making.

Prediction of phosphorylation sites based on granular support vector machine

Protein phosphorylation is the most extensive and important post-translational modification in eukaryotes, regulating the activity of almost all cells. Experimental methods used to identify phosphorylation sites, such as mass spectrometry, are costly and time-consuming. A number of algorithms have been developed to predict phosphorylation sites. However, they often select small data volume by random sampling. This cannot make full use of the characteristics of the entire data set to build a prediction model. According to the granularity calculation combined with the kernel fuzzy C-means clustering, this paper maps the massive raw data to a high-dimensional kernel space, and then divides the grains by clustering to obtain high-dimensional equilibrium grains. In particular, a specific granular support vector machine (KFCC–GSVM) prediction model is built in equilibrium grain data. This novel model improves the rationality and reliability of phosphorylation site data compression, so that the compressed data has the same distribution in the kernel space as the pre-compression data when applying the traditional SVM algorithm classification. Experimental results demonstrate that our method is better than the SVM-based non-kinase-specific phosphorylation site prediction method—Musite and the traditional GSVM method.

New Complexing Polymer Aminothioether Sorbents in the Analytical Control of Recyclable Metal-Containing Raw Material of Rare and Noble Metals

The development and use of thioether sorbents for the separation and preconcentration of metals from aqueous solutions are described. The possibilities of initial polymer thioethers in selective preconcentration and the acceleration of sorption through the introduction of nitrogen-containing groups into thioethers are considered. The dependence of the selectivity of sorbents on the nature of radical at the nitrogen atom is revealed. The use of aminothioethers with different substituents at the nitrogen atom enables distinguishing the groups of noble metals, toxic elements of arsenic and selenium subgroups, and a number of nonferrous metals in the presence of large excesses of other elements. A high recovery of the elements to be determined into the sorption preconcentrate and the selectivity of the sorbents help to improve the accuracy of the analysis. Prospects for the use of aminothioethers are considered on an example of sewage waters. It is found that the extraction of small concentrations of elements from large volumes of solutions is more convenient to carry out in dynamics, for which powdered aminothioether sorbents are not suitable. To ensure a possibility of using aminothioethers in dynamics, a “snake in a cage” synthesis method is used, in which the inert granular support is modified with aminothioether moieties. The introduction of aminothioethers with different radicals at the nitrogen atom makes it possible to combine sorbents selective with respect to different groups of elements on one carrier and thus to create a universal sorbent with predetermined properties, aimed at extracting certain metals.

Full-Field Deformation Measurements in Liquid-like-Solid Granular Microgel Using Digital Image Correlation

This paper presents the experimental characterization of the in-plane deformation field at any depth within a granular support medium (GSM) called Carbomer 940 using digital image correlation (DIC) and particle image velocimetry (PIV). A method was developed to produce a 2D plane of randomly shaped speckles within the GSM for DIC. Four different needle diameters and four different speeds were used as test specimens representative of those utilized for 3D printing of soft matter in the GSM. The results can be used to determine dimensional tolerances and assessing interactions between multiple injection needles and acceptable spacing. The displacements in the direction of needle motion (u) and transverse (v) were obtained. Subsequently, the magnitudes were determined as a function of distance from the needle path and time history. Results show that near the needle there is a region of yielded/fluidized material and away from the needle path the material acts like a viscoelastic solid. Permanent deformation decreases with increased distance from the path and recovery is enhanced by reversing back through the path.