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Advanced in vitro tissue chip models can reduce and replace animal experimentation and may eventually support "on-chip" clinical trials. To realize this potential, however, tissue chip platforms must be both mass-produced and reconfigurable to allow for customized design. To address these unmet needs, we introduce an extension of our μSiM (microdevice featuring a silicon-nitride membrane) platform. The modular μSiM (m-μSiM) uses mass-produced components to enable rapid assembly and reconfiguration by laboratories without knowledge of microfabrication. We demonstrate the utility of the m-μSiM by establishing an hiPSC-derived blood-brain barrier (BBB) in bioengineering and non-engineering, brain barriers focused laboratories. We develop and validate in situ and sampling-based assays of small molecule diffusion as a measure of barrier function. BBB properties show excellent interlaboratory agreement and match expectations from literature, validating the m-μSiM as a platform for barrier models and demonstrating successful dissemination of components and protocols. We then demonstrate the ability to quickly reconfigure the m-μSiM for co-culture and immune cell transmigration studies through addition of accessories and/or quick exchange of components. Because the development of modified components and accessories is easily achieved, custom designs of the m-μSiM should be accessible to any laboratory desiring a barrier-style tissue chip platform. This article is protected by copyright. All rights reserved.

© 2020 Author(s). Characterization of the elasticity of biological cells is growing as a new way to gain insight into cell biology. Cell mechanics are related to most aspects of cellular behavior, and applications in research and medicine are broad. Current methods are often limited since they require physical contact or lack resolution. From the methods available for the characterization of elasticity, those relying on high frequency ultrasound (phonons) are the most promising because they offer label-free, high (even super-optical) resolution and compatibility with conventional optical microscopes. In this Perspective contribution, we review the state of the art of picosecond ultrasonics for cell imaging and characterization, particularly for Brillouin scattering-based methods, offering an opinion for the challenges faced by the technology. The challenges are separated into biocompatibility, acquisition speed, resolution, and data interpretation and are discussed in detail along with new results.

Large data centers host thousands of tenants and services, and today's data center networks are no longer simple end- to-end transfer fabrics. Tenants desire more functionality like strong performance isolation, latency control, load balancing, middlebox placement, and other services. As tenant performance requirements become more demanding, data center providers wish to increase the set of services they offer in an attempt to meet the demand and attract new customers. Critically, while the promise of cloud computing makes it straightforward to rent compute resources, there is no standard model for tenants to pick and choose the network resources and services they require. Traditionally, a single procedure governs the allocation of resources to tenant services. For efficiency, data center providers multiplex tenants across the physical infrastructure, leveraging end host virtualization to carve out well-specified units of isolated CPU, memory, and storage. In contrast, tenants typically receive loose, qualitative descriptions of network performance with a limited set of available features. This disparity is problematic for today's data centers, which need to satisfy services with a diverse and non-uniform set of networking needs. This dissertation proposes to resolve this contention by provisioning a custom virtual network for each of the data center's clients. It presents task switching, a framework that redefines how tenants interact with and obtain resources from the network by servicing their individual requests for virtual network tasks. It then describes a model with two components, Blender and Omakase, that defines interfaces for the network provider to implement differing features and resource allocation procedures that tenants may choose to adopt for their tasks. Blender focuses on performance isolation, eliminating the either/or balancing act between resource efficiency and performance predictability by allowing data center operators to run multiple performance isolation models simultaneously. Omakase augments the Blender model to incorporate general purpose features for tasks, including customized in- network middlebox processing, failure resolution, and flow scheduling. We evaluate the architecture with a prototype implementation that addresses its correctness, scalability, and performance. We show that our SDN-based prototype can efficiently express many recently proposed performance isolation models and network features