Particle-based System Research Articles

Morphology of the scaphotrapeziotrapezoid joint: A multi-domain statistical shape modeling approach.

The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three-dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi-domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM-generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three-dimensional triangular surface meshes. A multi-domain, particle-based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface-normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities-a proxy for joint space-were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging-derived STT joint space.

Classification of complex local environments in systems of particle shapes through shape symmetry-encoded data augmentation.

Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using traditional approaches. However, the application of machine learning to self-assembly on systems of particle shapes is still underexplored. To address this gap, we propose a simple, physics-agnostic, yet powerful approach that involves training a multilayer perceptron (MLP) as a local environment classifier for systems of particle shapes, using input features such as particle distances and orientations. Our MLP classifier is trained in a supervised manner with a shape symmetry-encoded data augmentation technique without the need for any conventional roto-translations invariant symmetry functions. We evaluate the performance of our classifiers on four different scenarios involving self-assembly of cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with varying aspect ratios, and truncated shapes with different degrees of truncation. The proposed training process and data augmentation technique are both straightforward and flexible, enabling easy application of the classifier to other processes involving particle orientations. Our work thus presents a valuable tool for investigating self-assembly processes on systems of particle shapes, with potential applications in structure identification of any particle-based or molecular system where orientations can be defined.

A spatial measure-valued model for radiation-induced DNA damage kinetics and repair under protracted irradiation condition

In the present work, we develop a general spatial stochastic model to describe the formation and repair of radiation-induced DNA damage. The model is described mathematically as a measure-valued particle-based stochastic system and extends in several directions the model developed in Cordoni et al. (Phys Rev E 103:012412, 2021; Int J Radiat Biol 1–16, 2022a; Radiat Res 197:218–232, 2022b). In this new spatial formulation, radiation-induced DNA damage in the cell nucleus can undergo different pathways to either repair or lead to cell inactivation. The main novelty of the work is to rigorously define a spatial model that considers the pairwise interaction of lesions and continuous protracted irradiation. The former is relevant from a biological point of view as clustered lesions are less likely to be repaired, leading to cell inactivation. The latter instead describes the effects of a continuous radiation field on biological tissue. We prove the existence and uniqueness of a solution to the above stochastic systems, characterizing its probabilistic properties. We further couple the model describing the biological system to a set of reaction–diffusion equations with random discontinuity that model the chemical environment. At last, we study the large system limit of the process. The developed model can be applied to different contexts, with radiotherapy and space radioprotection being the most relevant. Further, the biochemical system derived can play a crucial role in understanding an extremely promising novel radiotherapy treatment modality, named in the community FLASH radiotherapy, whose mechanism is today largely unknown.

A GPU-based hydrodynamic simulator with boid interactions

We present a hydrodynamic simulation system using the GPU compute shaders of DirectX for simulating virtual agent behaviors and navigation inside a smoothed particle hydrodynamical (SPH) fluid environment with real-time water mesh surface reconstruction. The current SPH literature includes interactions between SPH and heterogeneous meshes but seldom involves interactions between SPH and virtual boid agents. The contribution of the system lies in the combination of the parallel smoothed particle hydrodynamics model with the distributed boid model of virtual agents to enable agents to interact with fluids. The agents based on the boid algorithm influence the motion of SPH fluid particles, and the forces from the SPH algorithm affect the movement of the boids. To enable realistic fluid rendering and simulation in a particle-based system, it is essential to construct a mesh from the particle attributes. Our system also contributes to the surface reconstruction aspect of the pipeline, in which we performed a set of experiments with the parallel marching cubes algorithm per frame for constructing the mesh from the fluid particles in a real-time compute and memory-intensive application, producing a wide range of triangle configurations. We also demonstrate that our system is versatile enough for reinforced robotic agents instead of boid agents to interact with the fluid environment for underwater navigation and remote control engineering purposes.

Stability of protein particle based Pickering emulsions in various environments: Review on strategies to inhibit coalescence and oxidation.

The emerging research interests in fabrication of protein particles as soft-particle emulsifiers show the prospective potential of using protein particles in novel poly-phase dispersing food systems. This review first provides a comprehensive summary and analysis on the dominant role of key physicochemical properties of protein particles including wettability, morphology, surface charge and protein concentration on their emulsifying abilities to construct Pickering emulsions. It was found that the constructed emulsions showed high sensitivity to changes in pH, ionic strength and temperature (thermal and freeze-thaw treatment). Moreover, oxidation remains as a challenge for protein particle based Pickering emulsions during prolonged storage, reducing their acceptance in food products. Current strategies for improving the stability of these emulsions to variable aqueous conditions and variable temperatures, and restricting oxidation event are summarized. In summary, an "ideal" protein particle-based Pickering emulsion system is proposed, encompassing aspects of interfacial property, emulsion network and texture, and antioxidant enrichment, thus promoting industrial translation into novel food and nutraceutical products.

Gene Therapy Using Efficient Direct Lineage Reprogramming Technology for Neurological Diseases.

Gene therapy is an innovative approach in the field of regenerative medicine. This therapy entails the transfer of genetic material into a patient's cells to treat diseases. In particular, gene therapy for neurological diseases has recently achieved significant progress, with numerous studies investigating the use of adeno-associated viruses for the targeted delivery of therapeutic genetic fragments. This approach has potential applications for treating incurable diseases, including paralysis and motor impairment caused by spinal cord injury and Parkinson's disease, and it is characterized by dopaminergic neuron degeneration. Recently, several studies have explored the potential of direct lineage reprogramming (DLR) for treating incurable diseases, and highlighted the advantages of DLR over conventional stem cell therapy. However, application of DLR technology in clinical practice is hindered by its low efficiency compared with cell therapy using stem cell differentiation. To overcome this limitation, researchers have explored various strategies such as the efficiency of DLR. In this study, we focused on innovative strategies, including the use of a nanoporous particle-based gene delivery system to improve the reprogramming efficiency of DLR-induced neurons. We believe that discussing these approaches can facilitate the development of more effective gene therapies for neurological disorders.

Learning the dynamics of particle-based systems with Lagrangian graph neural networks

Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics. However, traditional approaches require the knowledge of several abstract quantities such as the energy or force to infer the dynamics of these particles. Here, we present a framework, namely, Lagrangian graph neural network (LGnn), that provides a strong inductive bias to learn the Lagrangian of a particle-based system directly from the trajectory. We test our approach on challenging systems with constraints and drag—LGnn outperforms baselines such as feed-forward Lagrangian neural network (Lnn) with improved performance. We also show the zero-shot generalizability of the system by simulating systems two orders of magnitude larger than the trained one and also hybrid systems that are unseen by the model, a unique feature. The graph architecture of LGnn significantly simplifies the learning in comparison to Lnn with ∼25 times better performance on ∼20 times smaller amounts of data. Finally, we show the interpretability of LGnn, which directly provides physical insights on drag and constraint forces learned by the model. LGnn can thus provide a fillip toward understanding the dynamics of physical systems purely from observable quantities.

Deagglomeration of DNA nanomedicine carriers using controlled ultrasonication

Control over the agglomeration state of manufactured particle systems for drug and oligonucleotide intracellular delivery is paramount to ensure reproducible and scalable therapeutic efficacy. Ultrasonication is a well-established mechanism for the deagglomeration of bulk powders in dispersion. Its use in manufacturing requires strict control of the uniformity and reproducibility of the cavitation field within the sample volume to minimise within-batch and batch-to-batch variability. In this work, we demonstrate the use of a reference cavitating vessel which provides stable and reproducible cavitation fields over litre-scale volumes to assist the controlled deagglomeration of a novel non-viral particle-based plasmid delivery system. The system is the Nuvec delivery platform, comprising polyethylenimine-coated spiky silica particles with diameters of ∼ 200 nm. We evaluated the use of controlled cavitation at different input powers and stages of preparation, for example before and after plasmid loading. Plasmid loading was confirmed by X-ray photoelectron spectroscopy and gel electrophoresis. The latter was also used to assess plasmid integrity and the ability of the particles to protect plasmid from potential degradation caused by the deagglomeration process. We show the utility of laser diffraction and differential centrifugal sedimentation in quantifying the efficacy of product de-agglomeration in the microscale and nanoscale size range respectively. Transmission electron microscopy was used to assess potential damages to the silica particle structure due to the sonication process.

Particle-to-fluid direct-contact counter-flow heat exchanger: Simple-models validation and integration with a particle-based central tower system

This paper presents validation studies of two theoretical models of a direct-contact counter-flow particle-to-air heat exchanger. It also describes the integration of the proposed heat exchanger with a particle-based central tower system. Two basic models are analyzed: (1) the mixing model, which treats falling particles as an isolated particle falling downward in unbounded-atmospheric air, and (2) the simple-equilibrium model (SEM), which assumes that both the media reached thermal equilibrium in every segment along the exchanger. The mixing model predicted the outlet temperature quite accurately for the particles with 0.5 mm diameter. For CARBOEAD particles of 0.5 mm diameter, the predicted heat rate was within ±5% of the experimental results. The SEM overpredicted the outlet temperature regardless of particle size. A simple modification was added to the SEM, which led to improve the model predictions greatly. An integrated conceptual system design is also presented and discussed. The use of a double dump valve introduces many advantages towards a practical integrated system. Finally, employing particle strainers at the exchanger outlet is crucial to reduce/eliminate risks about the swept particles.

An Experimental Demonstration of the Effective Application of Thermal Energy Storage in a Particle-Based CSP System

Tests were performed at the particle-based CSP test facility at King Saud University to demonstrate a viable solution to overcome the limitations of using molten salt as a working medium in power plants. The KSU facility is composed of a heliostat field, particle heating receiver (PHR) at the top of a tower, thermal energy storage (TES) bin, a particle-to-working fluid heat exchanger (PWFHX), power cycle (microturbine), and a particle lift. During pre-commissioning, a substantial portion of the collected solar energy was lost during particle flow through the TES bin. The entrained air is shown to be the primary cause of such heat loss. The results show that the particle temperature at the PHR outlet can reach 720 °C after mitigating the entrained air issue. Additionally, during on-sun testing, a higher temperature of the air exiting the PWFHX than that of the air entering is observed, which indicates the effective solar contribution. Half-hour plant operation through stored energy was demonstrated after heliostat defocusing. Lastly, a sealable TES bin configuration for 1.3 MWe pre-commercial demonstration unit to be built in Saudi Arabia by Saudi Electric Company (SEC) is presented. This design modification has addressed the heat loss, pressure build-up, and contamination issues during TES charging.

Sensitivity Analysis of the Levelized Cost of Electricity for a Particle-Based Concentrating Solar Power System

Abstract This study presents a sensitivity analysis of the levelized cost of electricity (LCOE) for a particle-based system with the costs of the most current components. New models for the primary heat exchanger, thermal energy storage, and tower are presented and used to establish lower and upper bounds for these three components. The rest of component costs such as particle cost, cavity cost, and lift cost are set to lower and upper bounds estimating an uncertainty between 25% and 50%. Other relevant parameters related to lift and storage performance are also included in the analysis with the same uncertainty. This study also includes an upgrade to the receiver model by including the wind effect in the efficiency, which was not included in previous publications and may have a big impact in the system design. A parametric analysis shows the optimum values of solar multiple, storage hours, tower height, and concentration ratio, and a probabilistic analysis provides a cumulative distribution function for a range of LCOE values. The results show that the LCOE could be below $0.06/kWh with a probability of between 80% and 90%, where the costs of primary heat exchanger, particles, and lifts have largest contribution to the variance of the LCOE.

Preliminary Component Design and Cost Estimation of a Novel Electric-Thermal Energy Storage System Using Solid Particles

Abstract Energy storage will become indispensable to complement the uncertainty of intermittent renewable resources and to firm the electricity supply as renewable power generation becomes the mainstream new-built energy source and fossil fuel power plants are phased out to meet carbon-neutral utility targets. Current energy storage methods based on pumped storage hydropower or batteries have many limitations. Thermal energy storage (TES) has unique advantages in scale and siting flexibility to provide grid-scale storage capacity. A particle-based TES system is projected to have promising cost and performance characteristics to meet the future growing energy storage needs. This paper introduces the system and components required for particle TES to become technically and economically competitive. The system integrates electric particle heaters, particle TES within insulated concrete silos, and an efficient air-Brayton combined-cycle power system to provide power for storage durations up to several days via low-cost, high-performance storage cycles. Design specifications and cost estimation of major components in a commercial-scale system are presented in this paper. A techno-economic analysis based on preliminary component designs and performance indicates that particle TES integrated with an air-Brayton combined-cycle power system has a path to achieve the targeted levelized cost of storage of 5 ¢/kWh-cycle at a round-trip efficiency of 50% when taking low-cost energy-specific components and leveraging basic assets from existing thermal power plants. The cost model provides insights for further development and economic potentials for long-duration energy storage.

CRISPR/Cas9 technology for improving agronomic traits and future prospective in agriculture.

In this review, we have focused on the CRISPR/Cas9 technology for improving the agronomic traits in plants through point mutations, knockout, and single base editing, and we highlighted the recent progress in plant metabolic engineering. CRISPR/Cas9 technology has immense power to reproduce plants with desired characters and revolutionizing the field of genome engineering by erasing the barriers in targeted genome editing. Agriculture fields are using this advance genome editing tool to get the desired traits in the crops plants such as increase yield, improve product quality attributes, and enhance resistance against biotic and abiotic stresses by identifying and editing genes of interest. This review focuses on CRISPR/Cas-based gene knockout for trait improvement and single base editing to boost yield, quality, stress tolerance, and disease resistance traits in crops. Use of CRISPR/Cas9 system to facilitate crop domestication and hybrid breeding are also touched. We summarize recent developments and up-gradation of delivery mechanism (nanotechnology and virus particle-based delivery system) and progress in multiplex gene editing. We also shed lights in advances and challenges of engineering the important metabolic pathways that contain a variety of dietary metabolites and phytochemicals. In addition, we endorsed substantial technical hurdles and possible ways to overcome the unpredictability of CRISPR/Cas technology for broader application across various crop species. We speculated that by making a strong interconnection among all genomic fields will give a gigantic bunt of knowledge to develop crop expressing desired traits.

A fully Lagrangian DEM-MPS mesh-free model for ice-wave dynamics

This paper develops and evaluates a novel three-dimensional fully-Lagrangian (particle-based) numerical model, based on the hybrid discrete element method (DEM) and moving particle semi-implicit (MPS) mesh-free techniques, for modeling the highly-dynamic ice-wave interactions. Both MPS and DEM belong to mesh-free Lagrangian (particle) techniques. The model considers ice-wave dynamics as a multiphase continuum-discrete system. While the MPS solves the continuum equations of free-surface flow in a Lagrangian particle-based domain, the DEM uses a multi-sphere Hertzian contact dynamic model to simulate the ice floes motion and interaction. The hybrid model predicts the motion and collision of ice floes as well as their interaction with water, boundaries, and any obstacle in their way. Considering the mesh-free Lagrangian nature of both DEM and MPS, the developed model has an inherent ability to predict the free-drift (absence of internal stress) movements of the ice floes, e.g., sliding, rolling, colliding, and piling-up, in violent free-surface flow. A small-scale and challenging experiment based on dam-break flow over dry and wet beds with floating block floes, which mimics the characteristics of an idealized jam release, has been conducted to provide useful and comprehensive quality data for the validation of the proposed model, as well as other numerical models. Experimental and numerical results of the free-surface profile and the position of the blocks are compared. The results show the ability of the model to numerically reproduce and predict the complex three-dimensional dynamic behavior of wave-ice floes interaction. Overall, this study is a first effort toward developing an ice-wave dynamic within a fully Lagrangian framework (i.e. both flow hydrodynamics and ice dynamics in the Lagrangian particle-based system), and its results can be extended to bring an in-depth understanding of the physics of the real-scale ice-wave or river ice dynamic problems in the future.

Demonstration Reactor System for the Indirect Solar-Thermochemical Reduction of Redox Particles—The Particle Mix Reactor

Abstract In contrast to thermal receivers that provide heat for steam cycles, in solar thermochemistry often receiver-reactors are used, where materials undergo a reaction while being irradiated by concentrated sunlight. When applied to two-step redox cycles, multiple processes take place in such receiver-reactors, though on different timescales. This leads to design compromises and to high technical requirements for the implementation. A concept for an indirect particle-based system for thermochemical cycles was therefore proposed in which the heat required for the reduction of redox particles is provided by inert heat transfer particles that absorb concentrated solar radiation in a dedicated particle receiver. The novel and central component in this indirect system is the particle mix reactor. It functions by mixing the two particle types for heat transfer and establishing a controlled atmosphere under decreased oxygen partial pressures in a common reactor chamber. The design of an experimental setup for demonstration and investigation of the particle mix reactor is presented in this work. Potential operation modes and design options for particle heater, mixing unit, and oxygen partial pressure decrease are discussed and illustrated. The selection of a mixer type is based on the homogeneity of the obtained mixture. It is supported by the use of discrete element method (DEM) simulations, which were compared to experimental results from a separate setup. Heat loss estimations for the mixing process in the selected mixer geometry are performed for alumina heat transfer particles and strontium iron oxide redox particles. The components' geometries, the overall experimental setup design, as well as operation steps are presented.

Annexin-coated particles induce antigen-specific immunosuppression

Apoptotic cells mediate the development of tolerogenic dendritic cells (DC) and thus facilitate induction and maintenance of peripheral tolerance. Following the identification of the evolutionary conserved annexin core domain (Anx) as a specific signal on apoptotic cells which antagonises Toll-like receptor (TLR) signalling, we examined whether the tolerogenic capacity of Anx can be exploited to downregulate antigen-specific immune responses. The treatment of bone marrow-derived dendritic cells (BMDC) with particles harbouring Anx as well as the model antigen ovalbumin (OVA) attenuated the response of OVA-specific OT-II T cells. The co-culture of Anx-particle-treated DC and T cells resulted in an anergy-like phenotype characterized by reduced proliferation and cytokine secretion. Here we demonstrate that the anti-inflammatory effects of Anx which are mediated through DC can be used as a tool to generate a particle-based antigen delivery system that promotes antigen-specific immunosuppression. Such Anx-particles may be a new therapeutic approach for the treatment of autoimmune diseases.

부채꼴 영역 기반의 동적인 충돌 영역을이용한입자 기반충돌검사의 고속화기법

In this paper, we propose a framework that can perform acceleration collision detection efficiently by using a cone based collision area in a particle-based system which requires collision detection with many objects. Three conditions determine particle and cone-based collision regions: 1) If there is a cone position within the radius of the adjacent particle, 2) In the case where the position of the adjacent particle exists in the cone area, 3) When adjacent particles exist between two vectors forming a cone area. As a result, it is defined that when the above conditions are all satisfied, the particle and the region of a cone have collided. In this paper, we automatically update the area of the cone, which is the collision detection area, according to the particle movement. Determine the direction and length of the cone based on the position and velocity of the particle to calculate the dynamic change of the cone. Collision detection is performed quickly using only the particles in the finally calculated area. The acceleration method proposed in this paper is simple to implement because it is executed with a closed form equation instead of explicitly creating the tree data structure, and collision inspection performance is improved in all results

A responsive porous hydrogel particle-based delivery system for oncotherapy.

Liver cancer is one of the malignant cancers that seriously threatens human health. Although some common treatments including chemotherapy have been applied in oncotherapy, there are often serious shortcomings such as frequent and uncontrollable drug infusion. To overcome these limitations, here, we introduced responsive porous hydrogel microparticles loaded with 5-fluorouracil and metformin for oncotherapy. Because of the interconnected porous structures, various forms of active molecules could be loaded into the particles. In addition, the relatively higher temperature of the tumor site and the temperature-responsive shape transition of pNIPAM hydrogel enabled controllable drug release. The porous pNIPAM particles not only exhibited large loading efficiency and sustained release for the 5-fluorouracil and metformin co-delivery, but also protected drugs from being resolved. Thus, it can be anticipated that the porous microparticles will have great potential in oncotherapy.

MiR-122 and hepatocellular carcinoma: from molecular biology to therapeutics.

miR-122 and hepatocellular carcinoma: from molecular biology to therapeutics.

NUMERICAL INVESTIGATION OF BUBBLING FLUIDIZED BED TO BE USED AS THERMAL ENERGY STORAGE INTEGRATED TO HIGH-TEMPERATURE CONCENTRATED SOLAR POWER

A thermal energy storage unit designed to be used in a solid particle concentrated solar energy system is analyzed with the help of ANSYS Fluent 17.0. Hydrodynamics of the bubbling fluidized sand bed of 0.28 m × 1 m × 0.025 m dimensions to be used as a direct contact heat exchanger is modeled and validated. Geldart B-type particles with diameter of 275 micrometers and density of 2500 kg/m3 are used in modeling of bubbling fluidized sand bed. A Syamlal−O'Brien drag model with restitution coefficient of 0.99 and specularity coefficient of 0.1 predicts the reported experimental data well in terms of bed expansion ratio, temporal voidage profile, and pressure drop across the bed. According to thermal model results, a linear relation between interphase heat transfer coefficient and bed temperature is observed. A number of analyzed units are proposed as a particle-based storage system for the Ivanpah Solar Power Plant. It is shown that the system using an air Brayton cycle with thermal efficiency of 0.264 can produce 178 MW electricity for 11.22 h with 26,304 metric tons of silica sand that is required for daily storage.