Project description:Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of "learned sensing" applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden.
Project description:Protein ubiquitination is involved in virtually all cellular processes. Enrichment strategies employing antibodies targeting ubiquitin-derived diGly remnants combined with mass spectrometry (MS) have enabled investigations of ubiquitin signaling at a large scale. However, so far the power of data independent acquisition (DIA) with regards to sensitivity in single run analysis and data completeness have not yet been explored. Here, we develop a sensitive workflow combining diGly antibody-based enrichment and optimized Orbitrap-based DIA with comprehensive spectral libraries together containing more than 90,000 diGly peptides. This approach identifies 35,000 diGly peptides in single measurements of proteasome inhibitor-treated cells - double the number and quantitative accuracy of data dependent acquisition. Applied to TNF signaling, the workflow comprehensively captures known sites while adding many novel ones. An in-depth, systems-wide investigation of ubiquitination across the circadian cycle uncovers hundreds of cycling ubiquitination sites and dozens of cycling ubiquitin clusters within individual membrane protein receptors and transporters, highlighting new connections between metabolism and circadian regulation.
Project description:In mammalian systems "sterolomics" can be regarded as the quantitative or semi-quantitative profiling of all metabolites derived from cholesterol and its cyclic precursors. The system can be further complicated by metabolites derived from ingested phytosterols or pharmaceuticals, but this is beyond the scope of this article. "Sterolomics" can be performed on either an unbiased global format, or more usually, exploiting a targeted format. Here we discuss the different mass spectrometry-based analytical techniques used in "sterolomics" giving specific examples in the context of neurodegenerative disease and for the diagnosis of inborn errors of metabolism. We pay particular attention to the profiling of cholesterol metabolites in the bile acid biosynthesis pathways, although the analytical techniques discussed are also appropriate for analysis of hormonal steroids.
Project description:Natural minerals ('stone drugs') have been used in traditional Chinese medicines for over 2000 years, but there is potential for modern-day use of inorganic minerals to combat viral infections, antimicrobial resistance, and for other areas in need of new therapies and diagnostic aids. Metal and mineral surfaces on scales from milli-to nanometres, either natural or synthetic, are patterned or can be modified with hydrophilic/hydrophobic and ionic/covalent target-recognition sites. They introduce new strategies for medical applications. Such surfaces have novel properties compared to single metal centres. Moreover, 3D mineral particles (including hybrid organo-minerals) can have reactive cavities, and some minerals have dynamic movement of metal ions, anions, and other molecules within their structures. Minerals have a unique ability to interact with viruses, microbes and macro-biomolecules through multipoint ionic and/or non-covalent contacts, with potential for novel applications in therapy and biotechnology. Investigations of mineral deposits in biology, with their often inherent heterogeneity and tendency to become chemically-modified on isolation, are highly challenging, but new methods for their study, including in intact tissues, hold promise for future advances.
Project description:Fullerenes and related carbon based derivatives have shown a growing relevance in biology and medicine, mainly due to the unique electronic and structural properties that make them excellent candidates for multiple functionalization. This review focuses on the most recent developments of fullerene derivatives for different biological applications.
Project description:Here, we report a biomarker-free detection of various biological targets through a programmed machine learning algorithm and an automated computational selection process termed algorithmically guided optical nanosensor selector (AGONS). The optical data processed/used by algorithms are obtained through a nanosensor array selected from a library of nanosensors through AGONS. The nanosensors are assembled using two-dimensional nanoparticles (2D-nps) and fluorescently labeled single-stranded DNAs (F-ssDNAs) with random sequences. Both 2D-np and F-ssDNA components are cost-efficient and easy to synthesize, allowing for scaled-up data collection essential for machine learning modeling. The nanosensor library was subjected to various target groups, including proteins, breast cancer cells, and lethal-7 (let-7) miRNA mimics. We have demonstrated that AGONS could select the most essential nanosensors while achieving 100% predictive accuracy in all cases. With this approach, we demonstrate that machine learning can guide the design of nanosensor arrays with greater predictive accuracy while minimizing manpower, material cost, computational resources, instrumentation usage, and time. The biomarker-free detection attribute makes this approach readily available for biological targets without any detectable biomarker. We believe that AGONS can guide optical nanosensor array setups, opening broader opportunities through a biomarker-free detection approach for most challenging biological targets.
Project description:Bioenergetic function is characterized with assays obtained by polarographic systems. Analog systems without data acquisition, visualization, and processing tools are used but require cumbersome operations to derive respiration rate and ADP to oxygen stoichiometry of oxidative phosphorylation (ADP/O ratio). The analog signal of a polarograhic system (YSI-5300) was digitized and a graphical user interface (GUI) was developed in MATLAB to integrate visualization, recording, calibration and processing of bioenergetic data. With the GUI, the signal is continuously visualized during the experiment and respiratory rates and ADP/O ratios can be determined. The integrated system was tested to evaluate bioenergetic function of subpopulations of mitochondria isolated from rat skeletal muscle (n = 10). Signal processing was applied to denoise data recorded at the sampling rate of 1000Hz, and maximize data decimation for computational applications. The error in calculating the bioenergetic outputs using decimated data is negligible when data are denoised. The estimate of respiration rate, ADP/O ratio and RCR obtained with denoised data at sampling rate as low as 5Hz was similar to that obtained with raw data recorded at sampling rate of 1000Hz. In summary, the integrated tools of the GUI overcome the limitations of data processing, accuracy, and utilization of analog polarographic systems.
Project description:Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.