Search for: All records

Ciliates are powerful unicellular model organisms that have been used to elucidate fundamental biological processes. However, the high motility of ciliates presents a major challenge in studies using live-cell microscopy and microsurgery. While various immobilization methods have been developed, they are physiologically disruptive to the cell and incompatible with microscopy and/or microsurgery. Here, we describe a Simple Microfluidic Operating Room for the Examination and Surgery ofStentor coeruleus(SMORES). SMORES uses Quake valve-based microfluidics to trap, compress, and perform surgery onStentoras our model ciliate. Compared with previous methods, immobilization by physical compression in SMORES is more effective and uniform. The mean velocity of compressed cells is 24 times less than that of uncompressed cells. The compression is minimally disruptive to the cell and is easily applied or removed using a 3D-printed pressure rig. We demonstrate cell immobilization for up to 2 h without sacrificing cell viability. SMORES is compatible with confocal microscopy and is capable of media exchange for pharmacokinetic studies. Finally, the modular design of SMORES allows laser ablation or mechanical dissection of a cell into many cell fragments at once. These capabilities are expected to enable biological studies previously impossible in ciliates and other motile species.

 

Various template‐based and template‐free approaches have been proposed for single‐step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data‐driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry‐aware retrosynthesis prediction framework that combines powerful data‐driven models with prior domain knowledge. We present a tree‐to‐sequence transformer architecture that utilizes hierarchical SMILES grammar‐based trees, incorporating crucial chemistry information that is often overlooked by SMILES text‐based representations, such as local structures and functional groups. The proposed framework, grammar‐based molecular attention tree transformer (G‐MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G‐MATT achieves a promising top‐1 accuracy of 51% (top‐10 accuracy of 79.1%), an invalid rate of 1.5%, and a bioactive similarity rate of 74.8% on the USPTO‐50K dataset. Additional analyses of G‐MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures.

 

Stentor coeruleus , a single-cell ciliated protozoan, is a model organism for wound healing and regeneration studies. Despite Stentor 's large size (up to 2 mm in extended state), microdissection of Stentor remains challenging. In this work, we describe a hydrodynamic cell splitter, consisting of a microfluidic cross junction, capable of splitting Stentor cells in a non-contact manner at a high throughput of ∼500 cells per minute under continuous operation. Introduction of asymmetry in the flow field at the cross junction leads to asymmetric splitting of the cells to generate cell fragments as small as ∼8.5 times the original cell size. Characterization of cell fragment viability shows reduced 5-day survival as fragment size decreases and as the extent of hydrodynamic stress imposed on the fragments increases. Our results suggest that cell fragment size and composition, as well as mechanical stress, play important roles in the long-term repair of Stentor cells and warrant further investigations. Nevertheless, the hydrodynamic splitter can be useful for studying phenomena immediately after cell splitting, such as the closure of wounds in the plasma membrane which occurs on the order of 100–1000 seconds in Stentor . 

Variational approaches are among the most powerful techniques toapproximately solve quantum many-body problems. These encompass bothvariational states based on tensor or neural networks, and parameterizedquantum circuits in variational quantum eigensolvers. However,self-consistent evaluation of the quality of variational wavefunctionsis a notoriously hard task. Using a recently developed Hamiltonianreconstruction method, we propose a multi-faceted approach to evaluatingthe quality of neural-network based wavefunctions. Specifically, weconsider convolutional neural network (CNN) and restricted Boltzmannmachine (RBM) states trained on a square latticespin-1/2$1/2$J_1\!-\!J_2$J_1-J_2$Heisenberg model. We find that the reconstructed Hamiltonians aretypically less frustrated, and have easy-axis anisotropy near the highfrustration point. In addition, the reconstructed Hamiltonians suppressquantum fluctuations in the largeJ_2$J_2$limit. Our results highlight the critical importance of thewavefunction’s symmetry. Moreover, the multi-faceted insight from theHamiltonian reconstruction reveals that a variational wave function canfail to capture the true ground state through suppression of quantumfluctuations.

 

Abstract Background Wound healing is one of the defining features of life and is seen not only in tissues but also within individual cells. Understanding wound response at the single-cell level is critical for determining fundamental cellular functions needed for cell repair and survival. This understanding could also enable the engineering of single-cell wound repair strategies in emerging synthetic cell research. One approach is to examine and adapt self-repair mechanisms from a living system that already demonstrates robust capacity to heal from large wounds. Towards this end, Stentor coeruleus , a single-celled free-living ciliate protozoan, is a unique model because of its robust wound healing capacity. This capacity allows one to perturb the wounding conditions and measure their effect on the repair process without immediately causing cell death, thereby providing a robust platform for probing the self-repair mechanism. Results Here we used a microfluidic guillotine and a fluorescence-based assay to probe the timescales of wound repair and of mechanical modes of wound response in Stentor . We found that Stentor requires ~ 100–1000 s to close bisection wounds, depending on the severity of the wound. This corresponds to a healing rate of ~ 8–80 μm 2 /s, faster than most other single cells reported in the literature. Further, we characterized three distinct mechanical modes of wound response in Stentor : contraction, cytoplasm retrieval, and twisting/pulling. Using chemical perturbations, active cilia were found to be important for only the twisting/pulling mode. Contraction of myonemes, a major contractile fiber in Stentor , was surprisingly not important for the contraction mode and was of low importance for the others. Conclusions While events local to the wound site have been the focus of many single-cell wound repair studies, our results suggest that large-scale mechanical behaviors may be of greater importance to single-cell wound repair than previously thought. The work here advances our understanding of the wound response in Stentor and will lay the foundation for further investigations into the underlying components and molecular mechanisms involved. 

Micro-blade design is an important factor in the cutting of single cells and other biological structures. This paper describes the fabrication process of three-dimensional (3D) micro-blades for the cutting of single cells in a microfluidic “guillotine” intended for fundamental wound repair and regeneration studies. Our microfluidic guillotine consists of a fixed 3D micro-blade centered in a microchannel to bisect cells flowing through. We show that the Nanoscribe two-photon polymerization direct laser writing system is capable of fabricating complex 3D micro-blade geometries. However, structures made of the Nanoscribe IP-S resin have low adhesion to silicon, and they tend to peel off from the substrate after at most two times of replica molding in poly(dimethylsiloxane) (PDMS). Our work demonstrates that the use of a secondary mold replicates Nanoscribe-printed features faithfully for at least 10 iterations. Finally, we show that complex micro-blade features can generate different degrees of cell wounding and cell survival rates compared with simple blades possessing a vertical cutting edge fabricated with conventional 2.5D photolithography. Our work lays the foundation for future applications in single cell analyses, wound repair and regeneration studies, as well as investigations of the physics of cutting and the interaction between the micro-blade and biological structures. 

Traditional parallel-jaw grippers are insufficient for delicate object manipulation due to their stiffness and lack of dexterity. Other dexterous robotic hands often have bulky fingers, rely on complex time-varying cable drives, or are prohibitively expensive. In this paper, we introduce a novel low-cost compliant gripper with two centimeter-scaled 3-DOF delta robots using off-the-shelf linear actuators and 3D-printed soft materials. To model the kinematics of delta robots with soft compliant links, which diverge from typical rigid links, we train neural networks using a perception system. Furthermore, we analyze the delta robot’s force profile by varying the starting position in its workspace and measuring the resulting force from a push action. Finally, we demonstrate the compliance and dexterity of our gripper through six dexterous manipulation tasks involving small and delicate objects. Thus, we present the groundwork for creating modular multi-fingered hands that can execute precise and low-inertia manipulations. 

Two of the Coronavirus Disease 2019 (COVID-19) vaccines currently approved in the United States require 2 doses, administered 3 to 4 weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose or to continue with the recommended 2-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these 2 vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of preexisting immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% credible interval [CrI]: 7.8–29.7) infections, 0.69 (95% CrI: 0.52–0.97) hospitalizations, and 0.34 (95% CrI: 0.25–0.44) deaths per 10,000 population compared to the recommended 4-week interval between the 2 doses. Pfizer-BioNTech vaccines also averted an additional 0.60 (95% CrI: 0.37–0.89) hospitalizations and 0.32 (95% CrI: 0.23–0.45) deaths per 10,000 population in a 9-week delayed second dose (DSD) strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the 2 doses. 

Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.