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Shape displays which actively manipulate surface geometry are an expanding robotics domain with applications to haptics, manufacturing, aerodynamics, and more. However, existing displays often lack high-fidelity shape morphing, high-speed deformation, and embedded state sensing, limiting their potential uses. Here, we demonstrate a multifunctional soft shape display driven by a 10 × 10 array of scalable cellular units which combine high-speed electrohydraulic soft actuation, magnetic-based sensing, and control circuitry. We report high-performance reversible shape morphing up to 50 Hz, sensing of surface deformations with 0.1 mm sensitivity and external forces with 50 mN sensitivity in each cell, which we demonstrate across a multitude of applications including user interaction, image display, sensing of object mass, and dynamic manipulation of solids and liquids. This work showcases the rich multifunctionality and high-performance capabilities that arise from tightly-integrating large numbers of electrohydraulic actuators, soft sensors, and controllers at a previously undemonstrated scale in soft robotics.

 

Abstract Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow. 

With computing impacting most every professional field, it has become essential to provide pathways for students other than those majoring in computer science to acquire computing knowledge and skills. Virtually all employers and graduate and professional schools seek these skills in their employees or students, regardless of discipline. Academia currently leans towards approaches such as double majors or combined majors between computer science and other non-CS disciplines, commonly referred to as “CS+X” programs. These programs tend to require rigorous courses gleaned from the institutions’ courses for computer science majors. Thus, they may not meet the needs of majors in disciplines such as the social and biological sciences, humanities, and others. The University of Maryland, Baltimore County (UMBC) is taking an approach more suitably termed “X+CS” to fulfill the computing needs of non-CS majors. As part of a National Science Foundation (NSF) grant, we are developing a “computing” minor specifically to meet their needs. To date, we have piloted the first two of the minor’s approximately six courses. The first is a variation on the existing Computer Science I course required for majors but restricted to nonmajors. Both versions of the course use the Python language and cover the same programming content, but with the non-majors assigned projects with relevance to non-CS disciplines. We use the same student assessment measures of homework, projects, and examinations for both courses. After four semesters, results show that non-CS majors perform comparably to majors. Students also express increased interest in computing and satisfaction with being part of a non- CS major cohort. The second course was piloted in fall 2019. It is a new course intended to enhance and hone programming skills and introduce topics such as web scraping, HTML and CSS, web application development, data formats, and database use. Students again express increased interest in computing and were already beginning to apply the computing skills that they were learning to their non-CS courses. As a welcome side effect, we experienced a significant increase in the number of women and under-represented minorities (URMs) in these two courses when compared with CS-major specific courses. Overall, women comprised 52% of the population, with URMs following a similar upward trend. We are currently developing the third course in the computing minor and exploring options for the remaining three. Possibilities include electives from our Information Systems major. We will also be working with our science, social science, and humanities departments to utilize existing courses in those disciplines that apply computing. The student response that we have received thus far provides us with evidence that our computing minor will be popular among UMBC’s non-CS population, providing them with a more suitable and positive computing education than existing CS+X efforts. 

This report examines how sensing of substrate topography can be used to modulate T cell activation, a key coordinating step in the adaptive immune response. Inspired by the native T cell–antigen presenting cell interface, micrometer scale pits with varying depth are fabricated into planar substrates. Primary CD4⁺T cells extend actin‐rich protrusions into the micropits. T cell activation, reflected in secretion of cytokines interleukin‐2 and interferon gamma, is sensitive to the micropit depth. Surprisingly, arrays of micropits with 4 μm depth enhance activation compared to flat substrates but deeper micropits are less effective at increasing cell response, revealing a biphasic dependence in activation as a function of feature dimensions. Inhibition of cell contractility abrogates the enhanced activation associated with the micropits. In conclusion, this report demonstrates that the 3D, microscale topography can be used to enhance T cell activation, an ability that most directly can be used to improve production of these cells for immunotherapy.

 

Large‐scale bacteria culturing can present many challenges for small academic laboratories. To address these challenges a cost effective, laboratory scale bioreactor was designed and implemented. The constructed bioreactor addresses common problems that small or teaching‐focused laboratories face when attempting scale up cultures. The design utilizes materials commonly found in standard chemistry laboratories that are easily assembled with minor modifications. The system was validated through the replication of natural product production in shake flasks and the bioreactor. Additionally, measurements were done to ensure the designed bioreactor had comparablek_Lavalues to common shake flask conditions. We anticipate that this design will be of use to other small academic natural product groups as well as teaching laboratories as it offers an economical way to undertake large scale culturing of microorganisms.