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Much of modern molecular and cell biology research relies on the ability to purify cell types by fluorescence activated cell sorting (FACS). FACS typically relies on the ability to label cell types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for cell types without a priori knowledge of cell type markers. Here we propose GateID, a computational method that combines single-cell transcriptomics with FACS index sorting to purify cell types of choice, using only native cellular properties such as cell size, granularity and mitochondrial content. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.

SUMMARYCell fate transitions are essential for specialization of stem cells and their niches, but the precise timing and sequence of molecular events during embryonic development are largely unknown. Here, we show that dermal condensates (DC), signaling niches for epithelial progenitors in hair placodes, are specified before niche formation and function. With 3D/4D microscopy we identify unclustered DC precursors. With population-based and single-cell transcriptomics we define a molecular time-lapse of dynamic niche signatures and the developmental trajectory as the DC lineage emerges from fibroblasts. Co-expression of downregulated fibroblast and upregulated DC genes in niche precursors reveals a transitory molecular state following a proliferation shutdown. Waves of transcription factor and signaling molecule expression then consolidate DC niche formation. Finally, ablation of epidermal Wnt signaling and placode-derived FGF20 demonstrates their requirement for DC-precursor specification. These findings uncover a progenitor-dependent niche precursor fate and the transitory molecular events controlling niche formation and function.Graphical AbstractHIGHLIGHTSPrecursors of the hair follicle niche are specified before niche cluster formationBulk/single cell RNA-seq defines early niche fate at molecular transitional stateSuccessive waves of transcription factor/signaling genes mark niche fate acquisitionNiche fate acquisition is not “pre-programmed” and requires FGF20 from progenitors

Cell fate transitions are essential for specification of stem cells and their niches, but the precise timing and sequence of molecular events during embryonic development are largely unknown. Here, we identify, with 3D and 4D microscopy, unclustered precursors of dermal condensates (DC), signaling niches for epithelial progenitors in hair placodes. With population-based and single-cell transcriptomics, we define a molecular time-lapse from pre-DC fate specification through DC niche formation and establish the developmental trajectory as the DC lineage emerges from fibroblasts. Co-expression of downregulated fibroblast and upregulated DC genes in niche precursors reveals a transitory molecular state following a proliferation shutdown. Waves of transcription factor and signaling molecule expression then coincide with DC formation. Finally, ablation of epidermal Wnt signaling and placode-derived FGF20 demonstrates their requirement for pre-DC specification. These findings uncover a progenitor-dependent niche precursor fate and the transitory molecular events controlling niche formation and function.

Cost-effective and reliable damage detection is critical for the utilization of composite materials. This paper presents part of an experimental and analytical survey of candidate methods for the in situ detection of damage in composite materials. The experimental results are presented for the application of modal analysis techniques applied to graphite/epoxy specimens containing representative damage modes. Changes in natural frequencies and modes were found using a laser vibrometer, and 2-D finite element models were created for comparison with the experimental results. The models accurately predicted the response of the specimens at low frequencies, but coalescence of higher frequency modes makes mode-dependant damage detection difficult for structural applications. The frequency response method was found to be reliable for detecting even small amounts of damage in a simple composite structure, however the potentially important information about damage type, size, location and orientation were lost using this method since several combinations of these variables can yield identical response signatures.

The effects that film thickness has on the length scale over which dislocation motion takes place were studied to elucidate the origin of the thickness dependence of the strength of polycrystalline Ag thin films on substrates. At low temperatures (below 200°C), where dislocation-mediated plasticity is the dominant inelastic mechanism, the thickness dependence of two plasticity parameters, the activation volume and the length of mobile dislocation segments, was investigated. The experimental results indicate that the distance between pinning points along the length of mobile dislocations increases with film thickness, and that it is significantly smaller than the film thickness and the average grain size. The thickness-dependence of the length over which individual inelastic events occur, which is the characteristic length scale for dislocation plasticity, clarifies the origin of the dependence of the low-temperature strength of thin films on film thickness.

This work presents a comparison of three technologies for structural–acoustic control that, while prevalent in the literature, had not been compared on a single structure. The comparison is generalizable because the techniques are implemented on a panel structure representative of a more complex structure (e.g., an aircraft fuselage, a submarine vehicle hull, a satellite payload shroud, etc.). The test-bed used for this comparison is a carbon-fiber composite panel manufactured with embedded active fiber composite actuators. Since such integrated structures constitute a continued avenue of research, the manufacturing and performance of this structure is illustrated. The design of the test-bed is guided by an effort to achieve a dynamic response similar to a single panel in a typical aircraft or rotorcraft fuselage. Existing active control architectures for broadband acoustic radiation reduction are compared both analytically and experimentally on a representative structure to quantify the capabilities and limitations of the existing control methodologies. Specifically, three broad categories of control are compared: classical feedback (rate feedback), optimal feedback (linear quadratic Gaussian), and adaptive feedforward control (x -filtered least mean square). The control architectures implemented during this study are all single-input/single-output in order to allow a fair comparison of the issues involved in the design, as well as the use and performance of each approach. Both the vibration and the acoustic performance are recorded for each experiment under equivalent conditions to allow a generalizable comparison. Experimental results lead to conclusions pertaining to the application of active structural-based control to improve the acoustic performance of more complex structures.

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Accurate models are necessary in critical applications. Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This dissertation presents a new approach to update system models, accounting for these changes. The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data. Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of model parameters that yield the best match between experimental and analytical responses. Since the minimization procedure requires a fair amount of computation time, it makes the existing techniques infeasible for use as part of an adaptive control scheme correcting the model parameters as the system changes. They also require either mode shape expansion or model reduction before they can be applied, introducing errors in the procedure. Furthermore, none of the existing techniques has been applied to nonlinear systems. The neural network estimates the parameters being updated quickly and accurately without the need to measure all degrees of freedom of the system. This avoids the use of mode shape expansion or model reduction techniques, and allows for its implementation as part of an adaptive control scheme. The proposed technique is also capable of updating weakly nonlinear systems. Numerical simulations and experimental results show that the proposed method has good accuracy and generalization properties, and it is therefore, a suitable alternative for the solution of the model updating problem of this class of systems.