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Sieving specific particles from mixed samples is of great value in fields such as biochemistry and additive manufacturing. In this study, a particle sieving method for microfluidics was proposed based on a phononic crystal plate (PCP), the mechanism of which originates from the competition between the trapping effect of the resonant PCP-induced acoustic radiation force (ARF), disturbance effect of acoustic streaming (AS), and flushing effect of the continuous inlet flow on particles suspended in microfluidic channels. Specifically, particles with different sizes could be separated under inlet flow conditions owing to ARF and AS drag forces as functions of the particle diameter, incident acoustic pressure, and driving frequency. Furthermore, a comprehensive numerical analysis was performed to investigate the impacts of ARF, AS, and inlet flow conditions on the particle motion and sieving efficiency, and to explore proper operating parameters, including the acoustic pressure and inlet flow velocity. It was found that, for each inlet flow velocity, there was an optimal acoustic pressure allowing us to achieve the maximum sieving efficiency, but the sieving efficiency at a low flow velocity was not as good as that at a high flow velocity. Although a PCP with a high resonant frequency could weaken the AS, thereby suiting the sieving of small particles (<5 μm), a low channel height corresponding to a high frequency limits the throughput. Therefore, it is necessary to design a PCP with a suitable resonant frequency based on the size of the particles to be sieved. This investigation can provide guidance for the design of massive acoustic sorting mi-crofluidic devices based on phononic crystals or acoustic metamaterials under continuous flow.

Phytochrome B (phyB) is a plant photoreceptor that forms a membraneless organelle called a photobody. However, its constituents are not fully known. Here, we isolated phyB photobodies from Arabidopsis leaves using fluorescence-activated particle sorting and analyzed their components. We found that a photobody comprises ~1,500 phyB dimers along with other proteins that could be classified into two groups: The first includes proteins that directly interact with phyB and localize to the photobody when expressed in protoplasts, while the second includes proteins that interact with the first group proteins and require co-expression of a first-group protein to localize to the photobody. As an example of the second group, TOPLESS interacts with PHOTOPERIODIC CONTROL OF HYPOCOTYL 1 (PCH1) and localizes to the photobody when co-expressed with PCH1. Together, our results support that phyB photobodies include not only phyB and its primary interacting proteins but also its secondary interacting proteins. Phytochrome is a photoreceptor forming a membraneless organelle called a photobody. The authors isolated the photobody and found that the photobody is made of not only phytochrome but also its primary and secondary interacting proteins.

Spinning objects moving through air or a liquid experience a lift force—a phenomenon known as the Magnus effect. This effect is commonly exploited in ball sports but also is of considerable importance for applications in the aviation industry. Whereas Magnus forces are strong for large objects, they are weak at small scales and eventually vanish for overdamped micrometre-sized particles in simple liquids. Here we demonstrate a roughly one-million-fold enhanced Magnus force of spinning colloids in viscoelastic fluids. Such fluids are characterized by a time-delayed response to external perturbations, which causes a deformation of the fluidic network around the moving particle. When the particle also spins, the deformation field becomes misaligned relative to the particle’s moving direction, leading to a force perpendicular to the direction of travel and the spinning axis. Our uncovering of strongly enhanced memory-induced Magnus forces at microscales opens up applications for particle sorting and steering, and the creation and visualization of anomalous flows.The Magnus effect refers to rotating objects developing a lift force when travelling through a fluid. It normally vanishes at microscopic length scales but now a very large Magnus effect is demonstrated for spinning colloids in viscoelastic fluids.

We examine skyrmions under a dc drive interacting with a square array of obstacles for varied obstacle size and damping. When the drive is applied in a fixed direction, we find that the skyrmions are initially guided in the drive direction but also move transverse to the drive due to the Magnus force. The skyrmion Hall angle, which indicates the difference between the skyrmion direction of motion and the drive direction, increases with drive in a series of quantized steps as a result of the locking of the skyrmion motion to specific symmetry directions of the obstacle array. On these steps, the skyrmions collide with an integer number of obstacles to create a periodic motion. The transitions between the different locking steps are associated with jumps or dips in the velocity-force curves. In some regimes, the skyrmion Hall angle is actually higher than the intrinsic skyrmion Hall angle that would appear in the absence of obstacles. In the limit of zero damping, the skyrmion Hall angle is 90°, and we find that it decreases as the damping increases. For multiple interacting skyrmion species in the collective regime, we find jammed behavior at low drives where the different skyrmion species are strongly coupled and move in the same direction. As the drive increases, the species decouple and each can lock to a different symmetry direction of the obstacle lattice, making it possible to perform topological sorting in analogy to the particle sorting methods used to fractionate different species of colloidal particles moving over two-dimensional obstacle arrays.

Cryogenic electron microscopy (cryo-EM) is widely used to determine near-atomic resolution structures of biological macromolecules. Due to the low signal-to-noise ratio, cryo-EM relies on averaging many images. However, a crucial question in the field of cryo-EM remains unanswered: how close can we get to the minimum number of particles required to reach a specific resolution in practice? The absence of an answer to this question has impeded progress in understanding sample behavior and the performance of sample preparation methods. To address this issue, we develop an iterative particle sorting and/or sieving method called CryoSieve. Extensive experiments demonstrate that CryoSieve outperforms other cryo-EM particle sorting algorithms, revealing that most particles are unnecessary in final stacks. The minority of particles remaining in the final stacks yield superior high-resolution amplitude in reconstructed density maps. For some datasets, the size of the finest subset approaches the theoretical limit. Here the authors develop an iterative particle sieving method called CryoSieve, demonstrating this method outperforms other cryo-EM particle sorting algorithms to reveal that most particles are unnecessary in final stacks.

While vat photopolymerization has many advantages over soft lithography in fabricating microfluidic devices, including efficiency and shape complexity, it has difficulty achieving well-controlled micrometer-sized (smaller than 100 μm) channels in the layer building direction. The considerable light penetration depth of transparent resin leads to over-curing that inevitably cures the residual resin inside flow channels, causing clogs. In this paper, a 3D printing process — in-situ transfer vat photopolymerization is reported to solve this critical over-curing issue in fabricating microfluidic devices. We demonstrate microchannels with high Z -resolution (within 10 μm level) and high accuracy (within 2 μm level) using a general method with no requirements on liquid resins such as reduced transparency nor leads to a reduced fabrication speed. Compared with all other vat photopolymerization-based techniques specialized for microfluidic channel fabrication, our universal approach is compatible with commonly used 405 nm light sources and commercial photocurable resins. The process has been verified by multifunctional devices, including 3D serpentine microfluidic channels, microfluidic valves, and particle sorting devices. This work solves a critical barrier in 3D printing microfluidic channels using the high-speed vat photopolymerization process and broadens the material options. It also significantly advances vat photopolymerization’s use in applications requiring small gaps with high accuracy in the Z -direction. Despite many advantages of vat photopolymerization in microfluidic device fabrication, well-controlled μm-sized (< 100 μm) channels in the layer building direction remains a challenge. Here, authors present a general high resolution and low-cost 3D printing process that can produce devices within the 10 μm scale.

Block Yingmai 7 is structurally located in the western segment of the southern margin of the Kuqa Depression in the Tarim Basin. In the foreland basin, huge continental Mesozoic and Cenozoic strata have been deposited. In recent years, the Paleogene bottom sandstone section has been the main oil and gas exploration horizon. In order to further improve the oil and gas exploration effect of the Paleogene bottom sandstone in the study area and improve the accuracy of reservoir prediction, based on the related theory of sedimentology and petrology, this paper analyzes and studies the cores obtained by drilling in the Paleogene through laboratory core analysis experiments. The study shows that the Paleogene bottom sandstone is a braided river delta sedimentary system, and the sedimentary microfacies are mainly underwater distributary channels. The sandstone type is mainly light gray lithic feldspar fine sandstone, the sandstone debris particle sorting degree is medium-good, and the roundness is sub-edge-sub-circular. The microscopic characteristics of the reservoir are large pore throat radius, low displacement pressure, mainly intergranular pores, and good pore connectivity. It is a thin-layered mesoporous, medium-permeable, large-throat, and strongly heterogeneous reservoir. Finally, a classification evaluation table of reservoirs in block Yingmai 7 is established, and it is considered that the reservoirs of types I and II are enriched areas of oil and gas resources. The research results provide evidence for the prediction of oil and gas reservoirs.

We consider a moving target and an active pursing agent, modeled as an intelligent active Brownian particle capable of sensing the instantaneous target location and adjusting its direction of motion accordingly. An analytical and simulation study in two spatial dimensions reveals that pursuit performance depends on the interplay between self-propulsion, active reorientation, limited maneuverability, and random noise. Noise is found to have two opposing effects: (i) it is necessary to disturb regular, quasi-elliptical orbits around the target, and (ii) slows down pursuit by increasing the traveled distance of the pursuer. For a stationary target, we predict a universal scaling behavior of the mean pursuer–target distance and of the mean first-passage time as a function of Pe 2 /Ω, where the Péclet number Pe characterizes the activity and Ω the maneuverability. Importantly, the scaling variable Pe 2 /Ω depends implicitly on the level of thermal or active noise. A similar behavior is found for a moving target, but modified by the velocity ratio α = u 0 / v 0 of target and pursuer velocities u 0 and v 0 , respectively. We also propose a strategy to sort active pursuers according to their motility by circular target trajectories.

In recent years, the need for emergency resources has dramatically increased and it has caused an overcrowding problem for the emergency department (ED). Solving this problem by increasing medical resources is either impractical or infeasible. Thus, this manuscript develops a multi-objective mathematical model to allocate medical resources for the emergency department (ED). The optimal resource allocation is exploited by using some meta-heuristic algorithms, i.e., fast and elitism non-dominated sorting genetic algorithm (NSGA II), non-dominated sorting particle swarm algorithm (NSPSO), and non-dominated sorting differential evolution (NSDE). Thereafter, a dynamic simulation model, which embeds the solutions from the resource allocation model in the simulation process, is constructed. Each feasible solution from the three meta-heuristic algorithms is simulated to estimate the performance of the resources allocated in terms of the average service level and staff utilization. The results show that the performance of the NSGAII, where the average service level and staff utilization for the current resources are 0.844 and 0.751, respectively, is better than those of NSPSO and NSDE. Besides, the number of medical staff gives a significant effect on the service level and utilization while the number of beds only impacts staff utilization. The simulation model can find out that the best combination of the number of staff and the number of beds is from 1 to 10 staffs and 1–6 beds to maximize the utilization and service level.

Sojabohnen sind eine wichtige Kulturpflanze für die globale Ernährungssicherung. Die Sojabohnenerträge werden jedes Jahr durch zahlreiche Sojabohnenkrankheiten, insbesondere den Sojabohnen-Zystennematoden (SCN), reduziert. Es ist schwierig, das Vorhandensein von SCN im Feld visuell zu identifizieren, ganz zu schweigen von seiner Populationsdichte oder -zahl, da es keine offensichtlichen oberirdischen Krankheitssymptome gibt. Der einzige definitive Weg zur Bestimmung der SCN-Populationsdichte besteht darin, die SCN-Zysten direkt aus dem Boden zu extrahieren und dann die Eier aus den Zysten zu extrahieren und sie zu zählen. Die Extraktion wird typischerweise in kommerziellen Bodenanalyselabors und universitären Pflanzendiagnosekliniken durchgeführt und umfasst wiederholte Schritte des Siebens, Waschens, Sammelns, Mahlens und Reinigens. Hier präsentieren wir ein Roboterinstrument zur Reproduktion und Automatisierung der Funktionen der herkömmlichen Methoden zur Extraktion von Nematodenzysten aus dem Boden und zur anschließenden Extraktion von Eiern aus den wiedergewonnenen Nematodenzysten. Wir haben Mechanismen eingebaut, um das Bühnensystem zu betätigen, die Positionen einzelner Siebe mit dem Greifer zu manipulieren, Zysten und zystengroße Objekte aus im Wasser schwebender Erde zu bergen und die Zysten zu zermahlen, um ihre Eier freizusetzen. Alle Systemfunktionen werden über eine Touchscreen-Schnittstellensoftware gesteuert und bedient. Die Leistung des Roboterinstruments wird anhand von SCN-verseuchten Bodenproben von zwei Farmen an unterschiedlichen Standorten bewertet, und die Ergebnisse waren mit denen der herkömmlichen Technik vergleichbar. Unsere neue Technologie bringt die Vorteile der Automatisierung in die SCN-Bodendiagnostik, ein Schritt in Richtung einer langfristigen integrierten Schädlingsbekämpfung dieses schwerwiegenden Sojabohnenschädlings. Soybeans are an important crop for global food security. Every year, soybean yields are reduced by numerous soybean diseases, particularly the soybean cyst nematode (SCN). It is difficult to visually identify the presence of SCN in the field, let alone its population densities or numbers, as there are no obvious aboveground disease symptoms. The only definitive way to assess SCN population densities is to directly extract the SCN cysts from soil and then extract the eggs from cysts and count them. Extraction is typically conducted in commercial soil analysis laboratories and university plant diagnostic clinics and involves repeated steps of sieving, washing, collecting, grinding, and cleaning. Here we present a robotic instrument to reproduce and automate the functions of the conventional methods to extract nematode cysts from soil and subsequently extract eggs from the recovered nematode cysts. We incorporated mechanisms to actuate the stage system, manipulate positions of individual sieves using the gripper, recover cysts and cyst-sized objects from soil suspended in water, and grind the cysts to release their eggs. All system functions are controlled and operated by a touchscreen interface software. The performance of the robotic instrument is evaluated using soil samples infested with SCN from two farms at different locations and results were comparable to the conventional technique. Our new technology brings the benefits of automation to SCN soil diagnostics, a step towards long-term integrated pest management of this serious soybean pest. Le soja est une culture importante pour la sécurité alimentaire mondiale. Chaque année, les rendements du soja sont réduits par de nombreuses maladies du soja, en particulier le nématode à kyste du soja (NCS). Il est difficile d'identifier visuellement la présence de SCN sur le terrain, sans parler de ses densités ou de son nombre de population, car il n'y a pas de symptômes évidents de la maladie en surface. Le seul moyen définitif d'évaluer les densités de population de SCN est d'extraire directement les kystes de SCN du sol, puis d'extraire les œufs des kystes et de les compter. L'extraction est généralement effectuée dans des laboratoires commerciaux d'analyse des sols et des cliniques universitaires de diagnostic des plantes et implique des étapes répétées de tamisage, de lavage, de collecte, de broyage et de nettoyage. Nous présentons ici un instrument robotique pour reproduire et automatiser les fonctions des méthodes conventionnelles pour extraire les kystes de nématodes du sol et ensuite extraire les œufs des kystes de nématodes récupérés. Nous avons incorporé des mécanismes pour actionner le système de scène, manipuler les positions des tamis individuels à l'aide de la pince, récupérer des kystes et des objets de la taille d'un kyste du sol en suspension dans l'eau et broyer les kystes pour libérer leurs œufs. Toutes les fonctions du système sont contrôlées et exploitées par un logiciel d'interface à écran tactile. La performance de l'instrument robotique est évaluée à l'aide d'échantillons de sol infestés de SCN provenant de deux fermes situées à des endroits différents et les résultats sont comparables à la technique conventionnelle. Notre nouvelle technologie apporte les avantages de l'automatisation aux diagnostics de sol du SCN, une étape vers la lutte antiparasitaire intégrée à long terme de ce grave ravageur du soja.