Electroactive hydrogels (EAH) that exhibit large deformation in response to an electric field have received great attention as a potential actuating material for soft robots and artificial muscle. However, their application has been limited due to the use of traditional two-dimensional (2D) fabrication methods. Here we present soft robotic manipulation and locomotion with 3D printed EAH microstructures. Through 3D design and precise dimensional control enabled by a digital light processing (DLP) based micro 3D printing technique, complex 3D actuations of EAH are achieved. We demonstrate soft robotic actuations including gripping and transporting an object and a bidirectional locomotion.

A comprehensive cellular anatomy of normal human kidney is crucial to address the cellular origins of renal disease and renal cancer. Some kidney diseases may be cell type-specific, especially renal tubular cells. To investigate the classification and transcriptomic information of the human kidney, we rapidly obtained a single-cell suspension of the kidney and conducted single-cell RNA sequencing (scRNA-seq). Here, we present the scRNA-seq data of 23,366 high-quality cells from the kidneys of three human donors. In this dataset, we show 10 clusters of normal human renal cells. Due to the high quality of single-cell transcriptomic information, proximal tubule (PT) cells were classified into three subtypes and collecting ducts cells into two subtypes. Collectively, our data provide a reliable reference for studies on renal cell biology and kidney disease.

Digital 3D printing with a shape memory polymer is utilized to create mechanical metamaterials exhibiting dramatic and reversible changes in stiffness, geometry, and functions.

Microneedle (MN), a miniaturized needle with a length‐scale of hundreds of micrometers, has received a great deal of attention because of its minimally invasive, pain‐free, and easy‐to‐use nature. However, a major challenge for controlled long‐term drug delivery or biosensing using MN is its low tissue adhesion. Although microscopic structures with high tissue adhesion are found from living creatures in nature (e.g., microhooks of parasites, barbed stingers of honeybees, quills of porcupines), creating MNs with such complex microscopic features is still challenging with traditional fabrication methods. Here, a MN with bioinspired backward‐facing curved barbs for enhanced tissue adhesion, manufactured by a digital light processing 3D printing technique, is presented. Backward‐facing barbs on a MN are created by desolvation‐induced deformation utilizing cross‐linking density gradient in a photocurable polymer. Barb thickness and bending curvature are controlled by printing parameters and material composition. It is demonstrated that tissue adhesion of a backward‐facing barbed MN is 18 times stronger than that of barbless MN. Also demonstrated is sustained drug release with barbed MNs in tissue. Improved tissue adhesion of the bioinspired MN allows for more stable and robust performance for drug delivery, biofluid collection, and biosensing.

No abstract

In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.

No abstract

The high performance of FPGA (Field Programmable Gate Array) in image processing applications is justified by its flexible reconfigurability, its inherent parallel nature and the availability of a large amount of internal memories. Lately, the Stochastic Computing (SC) paradigm has been found to be significantly advantageous in certain application domains including image processing because of its lower hardware complexity and power consumption. However, its viability is deemed to be limited due to its serial bit stream processing and excessive run-time requirement for convergence. To address these issues, a novel approach is proposed in this work where an energy-efficient implementation of SC is accomplished by introducing fast-converging Quasi-Stochastic Number Generators (QSNGs) and parallel stochastic bit stream processing, which are well suited to leverage FPGA's reconfigurability and abundant internal memory resources. the proposed approach has been tested on the Virtex-4 FPGA, and results have been compared with the serial and parallel implementations of conventional stochastic computation using the well-known SC edge detection and multiplication circuits. Results prove that by using this approach, execution time, as well as the power consumption are decreased by a factor of 3.5 and 4.5 for the edge detection circuit and multiplication circuit, respectively.