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Pierre-Yves Burgi

Publications and source records attributed to Pierre-Yves Burgi.

2 recordsLinked to original sources

High-fidelity robotic PCR amplification

Polymerase chain reaction (PCR) underpins modern molecular biology, yet its deployment in emerging domains such as DNA data storage and distributed diagnostics remains constrained by bulky thermocyclers, complex thermal hardware, and contamination-prone workflows. Here, we present an autonomous robotic PCR platform that redefines thermocycling as a motion-controlled process rather than a temperature-controlled device. The system employs a programmable robotic liquid handler to execute PCR entirely within sealed pipette tips, repeatedly immersing and withdrawing reaction volumes in a single temperature-stabilized oil bath to realize denaturation, annealing, and extension steps through precise spatiotemporal control. This architecture eliminates conventional thermocyclers and enables fully enclosed reactions with complete sample recovery. We demonstrate that the robotic system achieves amplification efficiency and sequencing fidelity comparable to high-performance commercial thermocyclers when applied to DNA-encoded datasets. Beyond performance parity, the platform minimizes consumables consumption, suppresses cross-contamination through physical isolation, and supports parallelization through robotic scheduling rather than hardware duplication. Structural contamination control is demonstrated through sealed-tip confinement, validated by no-template control experiments under deliberate challenge conditions. By abstracting PCR thermocycling into a robotically orchestrated manipulation task, this work establishes a generalizable framework for automated biochemical processing and positions robotic control as a central design axis for scalable, low-cost molecular workflows.

q-bio.QM

Probabilistic Motion Estimation Based on Temporal Coherence

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence. This temporal grouping can be considered a generalization of the data association techniques used by engineers to study motion sequences. Our temporal-grouping theory is expressed in terms of the Bayesian generalization of standard Kalman filtering. To implement the theory we derive a parallel network which shares some properties of cortical networks. Computer simulations of this network demonstrate that our theory qualitatively accounts for psychophysical experiments on motion occlusion and motion outliers.

cs.CV