Pushing the Boundaries of Biomolecule Characterization through Deep Learning

The importance of studying biological molecules in living organisms can hardly be overstated as they regulate crucial processes in living matter of all kinds.Their ubiquitous nature makes them relevant for disease diagnosis, drug development, and for our fundamental understanding of the complex systems of biology.However, due to their small size, they scatter too little light on their own to be directly visible and available for study.Thus, it is necessary to develop characterization methods which enable their elucidation even in the regime of very faint signals. Optical systems, utilizing the relatively low intrusiveness of visible light, constitute one such approach of characterization. However, the optical systems currently capable of analyzing single molecules in the nano-sized regime today either require the species of interest to be tagged with visible labels like fluorescence or chemically restrained on a surface to be analyzed.Ergo, there exist effectively no methods of characterizing very small biomolecules under naturally relevant conditions through unobtrusive probing. Nanofluidic Scattering Microscopy is a method introduced in this thesis which bridges this gap by enabling the real-time label-free size-and-weight determination of freely diffusing molecules directly in small nano-sized channels. However, the molecule signals are so faint, and the background noise so complex with high spatial and temporal variation, that standard methods of data analysis are incapable of elucidating the molecules\u27 properties of relevance in any but the least challenging conditions.To remedy the weak signal, and realize the method\u27s full potential, this thesis\u27 focus is the development of a versatile deep-learning based computer-vision platform to overcome the bottleneck of data analysis. We find that said platform has considerably increased speed, accuracy, precision and limit of detection compared to standard methods, constituting even a lower detection limit than any other method of label-free optical characterization currently available. In this regime, hitherto elusive species of biomolecules become accessible for study, potentially opening up entirely new avenues of biological research. These results, along with many others in the context of deep learning for optical microscopy in biological applications, suggest that deep learning is likely to be pivotal in solving the complex image analysis problems of the present and enabling new regimes of study within microscopy-based research in the near future

Projected sensitivity to sub-GeV dark matter of next-generation semiconductor detectors

We compute the projected sensitivity to dark matter (DM) particles in the sub-GeV mass range of future direct detection experiments using germanium and silicon semiconductor targets. We perform this calculation within the dark photon model for DM-electron interactions using the likelihood ratio as a test statistic, Monte Carlo simulations, and background models that we extract from recent experimental data. We present our results in terms of DM-electron scattering cross section values required to reject the background only hypothesis in favour of the background plus DM signal hypothesis with a statistical significance, $\mathcal{Z}$, corresponding to 3 or 5 standard deviations. We also test the stability of our conclusions under changes in the astrophysical parameters governing the local space and velocity distribution of DM in the Milky Way. In the best-case scenario, when a high-voltage germanium detector with an exposure of $50$ kg-year and a CCD silicon detector with an exposure of $1$ kg-year and a dark current rate of $1\times10^{-7}$ counts/pixel/day have simultaneously reported a DM signal, we find that the smallest cross section value compatible with $\mathcal{Z}=3$ ($\mathcal{Z}=5$) is about $8\times10^{-42}$ cm$^2$ ($1\times10^{-41}$ cm$^2$) for contact interactions, and $4\times10^{-41}$ cm$^2$ ($7\times10^{-41}$ cm$^2$) for long-range interactions. Our sensitivity study extends and refine previous works in terms of background models, statistical methods, and treatment of the underlying astrophysical uncertainties.Comment: 17 pages, 4 figure

Label-free nanofluidic scattering microscopy of size and mass of single diffusing molecules and nanoparticles

Nanofluidic scattering microscopy enables label-free, quantitative measurements of the molecular weight and hydrodynamic radius of biological molecules and nanoparticles freely diffusing inside a nanofluidic channel. Label-free characterization of single biomolecules aims to complement fluorescence microscopy in situations where labeling compromises data interpretation, is technically challenging or even impossible. However, existing methods require the investigated species to bind to a surface to be visible, thereby leaving a large fraction of analytes undetected. Here, we present nanofluidic scattering microscopy (NSM), which overcomes these limitations by enabling label-free, real-time imaging of single biomolecules diffusing inside a nanofluidic channel. NSM facilitates accurate determination of molecular weight from the measured optical contrast and of the hydrodynamic radius from the measured diffusivity, from which information about the conformational state can be inferred. Furthermore, we demonstrate its applicability to the analysis of a complex biofluid, using conditioned cell culture medium containing extracellular vesicles as an example. We foresee the application of NSM to monitor conformational changes, aggregation and interactions of single biomolecules, and to analyze single-cell secretomes

Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air

Abstract Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H2 sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H2 limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the <1000 ppm US Department of Energy performance target. Furthermore, the sensors pass the ISO 26142:2010 stability requirement in 80% relative humidity in air down to 0.06% H2 and show no signs of performance loss after 140 h continuous operation. Our results thus demonstrate the potential of plasmonic hydrogen sensors for use in high humidity and how neural-network-based data treatment can significantly boost their performance

Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air

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