Search for: All records

We introduce an acoustic microfluidic platform that efficiently traps and selectively releases individual cells using spherical air cavities embedded in a polydimethylsiloxane (PDMS) substrate for large scale manipulation. 

Abstract The basal endosperm transfer layer (BETL) of the maize (Zea mays L.) kernel is composed of transfer cells for nutrient transport to nourish the developing kernel. To understand the spatiotemporal processes required for BETL development, we characterized 2 unstable factor for orange1 (Zmufo1) mutant alleles. The BETL defects in these mutants were associated with high levels of reactive oxygen species, oxidative DNA damage, and cell death. Interestingly, antioxidant supplementation in in vitro cultured kernels alleviated the cellular defects in mutants. Transcriptome analysis of the loss-of-function Zmufo1 allele showed differential expression of tricarboxylic acid cycle, redox homeostasis, and BETL-related genes. The basal endosperms of the mutant alleles had high levels of acetyl-CoA and elevated histone acetyltransferase activity. The BETL cell nuclei showed reduced electron-dense regions, indicating sparse heterochromatin distribution in the mutants compared with wild-type. Zmufo1 overexpression further reduced histone methylation marks in the enhancer and gene body regions of the pericarp color1 (Zmp1) reporter gene. Zmufo1 encodes an intrinsically disordered nuclear protein with very low sequence similarity to known proteins. Yeast two-hybrid and luciferase complementation assays established that ZmUFO1 interacts with proteins that play a role in chromatin remodeling, nuclear transport, and transcriptional regulation. This study establishes the critical function of Zmufo1 during basal endosperm development in maize kernels. 

Knowledge graphs (KGs), with their flexible encoding of heterogeneous data, have been increasingly used in a variety of applications. At the same time, domain data are routinely stored in formats such as spreadsheets, text, or figures. Storing such data in KGs can open the door to more complex types of analytics, which might not be supported by the data sources taken in isolation. Giving domain experts the option to use a predefined automated workflow for integrating heterogeneous data from multiple sources into a single unified KG could significantly alleviate their data-integration time and resource burden, while potentially resulting in higher-quality KG data capable of enabling meaningful rule mining and machine learning.In this paper we introduce a domain-agnostic workflow called BUILD-KG for integrating heterogeneous scientific and experimental data from multiple sources into a single unified KG potentially enabling richer analytics. BUILD-KG is broadly applicable, accepting input data in popular structured and unstructured formats. BUILD-KG is also designed to be carried out with end users as humans-in-the-loop, which makes it domain aware. We present the workflow, report on our experiences with applying it to scientific and experimental data in the materials science domain, and provide suggestions for involving domain scientists in BUILD-KG as humans-in-the-loop. 

Abstract Heat conduction in solids is typically governed by the Fourier’s law describing a diffusion process due to the short wavelength and mean free path for phonons and electrons. Surface phonon polaritons couple thermal photons and optical phonons at the surface of polar dielectrics, possessing much longer wavelength and propagation length, representing an excellent candidate to support extraordinary heat transfer. Here, we realize clear observation of thermal conductivity mediated by surface phonon polaritons in SiO₂nanoribbon waveguides of 20-50 nm thick and 1-10 μm wide and also show non-Fourier behavior in over 50-100 μm distance at room and high temperature. This is enabled by rational design of the waveguide to control the mode size of the surface phonon polaritons and its efficient coupling to thermal reservoirs. Our work laid the foundation for manipulating heat conduction beyond the traditional limit via surface phonon polaritons waves in solids. 

Abstract The reproductive success of birds is closely tied to the characteristics of their nests. It is crucial to understand the distribution of nest traits across phylogenetic and geographic dimensions to gain insight into bird evolution and adaptation. Despite the extensive historical documentation on breeding behavior, a structured dataset describing bird nest characteristics has been lacking. To address this gap, we have compiled a comprehensive dataset that characterizes three ecologically and evolutionarily significant nest traits—site, structure, and attachment—for 9,248 bird species, representing all 36 orders and 241 out of the 244 families. By defining seven sites, seven structures, and four attachment types, we have systematically classified the nests of each species using information from text descriptions, photos, and videos sourced from online databases and literature. This nest traits dataset serves as a valuable addition to the existing body of morphological and ecological trait data for bird species, providing a useful resource for a wide range of avian macroecological and macroevolutionary research. 

Acoustic patterning of micro-particles has many important biomedical applications. However, fabrication of such microdevices is costly and labor-intensive. Among conventional fabrication methods, photo-lithography provides high resolution but is expensive and time consuming, and not ideal for rapid prototyping and testing for academic applications. In this work, we demonstrate a highly efficient method for rapid prototyping of acoustic patterning devices using laser manufacturing. With this method we can fabricate a newly designed functional acoustic device in 4 hours. The acoustic devices fabricated using this method can achieve sub-wavelength, complex and non-periodic patterning of microparticles and biological objects with a spatial resolution of 60 μm across a large active manipulation area of 10 × 10 mm 2 . 

Abstract A new device termed “Optomagnetic Micromirror Arrays” (OMA) is demonstrated capable of mapping the stiffness distribution of biomimetic materials across a 5.1 mm × 7.2 mm field of view with cellular resolution. The OMA device comprises an array of 50 000 magnetic micromirrors with optical grating structures embedded beneath an elastic PDMS film, with biomimetic materials affixed on top. Illumination of a broadband white light beam onto these micromirrors results in the reflection of microscale rainbow light rays on each micromirror. When a magnetic field is applied, it causes each micromirror to tilt differently depending on the local stiffness of the biomimetic materials. Through imaging these micromirrors with low N.A. optics, a specific narrow band of reflection light rays from each micromirror is captured. Changing a micromirror's tilt angle also alters the color spectrum it reflects back to the imaging system and the color of the micromirror image it represents. As a result, OMA can infer the local stiffness of the biomimetic materials through the color change detected on each micromirror. OMA offers the potential for high‐throughput stiffness mapping at the tissue‐level while maintaining spatial resolution at the cellular level.