881 research outputs found
Constructing a mass accuracy surface can improve automatic annotations in LC-MS based metabolomics
No full textRecall this Book 30: Nir Eyal on (the Deontology of) "Challenge Testing" a Covid Vaccine
No full textOn April 27, David D. Kirkpatrick reported in the N. Y. Times that Oxford's Jenner Center is close to starting human trials on a potential Covid-19 vaccine. According to Kirkpatrick, "ethics rules, as a general principle, forbid seeking to infect human test participants with a serious disease. That means the only way to prove that a vaccine works is to inoculate people in a place where the virus spreading naturally around them." It ain't necessarily so, says Nir Eyal, Henry Rutgers Professor of Ethics and Director of Center for Population-Level Bioethics, Rutgers University. Eyal is lead author (along with Harvard's Marc Lipsitch and Peter Smith) of a striking March article in the Journal of Infectious Diseases, Human Challenge Studies to Accelerate Coronavirus Vaccine Licensure. A recent interview with Nir in Nature has a more revealing title: "Should scientists infect healthy people with the coronavirus to test vaccines?" So, John sat down with Nir to discuss the idea of deliberately exposing healthy young volunteers to corona virus in order to accelerate the efficacy phase of vaccine testing. Prior to this pandemic, many felt challenge testing with a deadly disease was beyond the ethical pale. Eyal et. al propose that despite its checkered history (think coerced deadly medical procedures), there is an interesting philosophical case to be made in its favor
Integrated approach for metabolite identification in LC-MS
No full textAnnotation of peaks and rapid identification of metabolites is currently still a major bottle-neck in mass spectrometry (MS) based metabolomics. Despite
numerous new algorithms and software packages published in recent years, including attempts at de-novo identification and modeling, the workhorse of
the trade remains mass-to-mass and retention time (RT) matching of observed and reference library peaks, including, when available, MS/MS data which
is used for confirmation. In this work, we present a rational, statistically based expansion of the used approach using orthogonal computational modules
and accumulation of independent evidence to achieve automatic high confidence identifications of metabolites in LC-MS data
A fully automated pipeline for the analysis of Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomics experiments.
No full text
Constructing a mass accuracy surface to improve automatic annotation in LC-MS based metabolomics
No full textAccurate mass measurement is essential for metabolite identification in the field of mass spectrometry (MS) based
metabolomics, since many unidentified peak signals are being resolved by mass-to-mass matching to reference databases.
Contrary to studies focused on the mass accuracy of MS instruments done in the past, which had a limited scope, the method
presented here uses a much larger amount of data to build a model which predicts MS mass accuracy as a function of the two
most influential parameters, namely: the mass value and the peak intensity. The widely used Synapt qTOF-MS instrument was
chosen to demonstrate the method. The output model gives the analytical chemist an option to estimate the mass
measurement accuracy on a peak-by-peak basis. We demonstrate that this can lead to a better performance in untargeted
metabolite annotation scenario
A Highly Photostable Hyperbranched Polyglycerol-Based NIR Fluorescence Nanoplatform for Mitochondria-Specific Cell Imaging
No full textConsidering the critical role of mitochondria in the life and death of cells, non-invasive long-term tracking of mitochondria has attracted considerable interest. However, a high-performance mitochondria-specific labeling probe with high photostability is still lacking. Herein a highly photostable hyperbranched polyglycerol (hPG)-based near-infrared (NIR) quantum dots (QDs) nanoplatform is reported for mitochondria-specific cell imaging. Comprising NIR Zn-Cu-In-S/ZnS QDs as extremely photostable fluorescent labels and alkyl chain (C-12)/triphenylphosphonium (TPP)-functionalized hPG derivatives as protective shell, the tailored QDs@hPG-C-12/TPP nanoprobe with a hydrodynamic diameter of about 65 nm exhibits NIR fluorescence, excellent biocompatibility, good stability, and mitochondria-targeted ability. Cell uptake experiments demonstrate that QDs@hPG-C-12/TPP displays a significantly enhanced uptake in HeLa cells compared to nontargeted QDs@hPG-C-12. Further co-localization study indicates that the probe selectively targets mitochondria. Importantly, compared with commercial deep-red mitochondria dyes, QDs@hPG-C-12/TPP possesses superior photostability under continuous laser irradiation, indicating great potential for long-term mitochondria labeling and tracking. Moreover, drug-loaded QDs@hPG-C-12/TPP display an enhanced tumor cell killing efficacy compared to nontargeted drugs. This work could open the door to the construction of organelle-targeted multifunctional nanoplatforms for precise diagnosis and high-efficient tumor therapy
Constructing a mass measurement error surface to improve automatic annotations in liquid chromatography/mass spectrometry based metabolomics
No full textRATIONALE Estimation of mass measurement accuracy is an elementary step in the application of mass spectroscopy (MS) data towards metabolite annotations and has been addressed several times in the past. However, the reproducibility of mass measurements over a diverse set of analytes and in variable operating conditions, which are common in high-throughput metabolomics studies, has, to the best of our knowledge, not been addressed so far. METHODS A method to automatically extract mass measurement errors from a large data set of measurements made on a quadrupole time-of-flight (QTOF) MS instrument has been developed. The size of the data processed in this study has enabled us to use a statistical data driven approach to build a model which reliably predicts the confidence interval of the absolute mass measurement error based on individual ion peak conditions in a fast, high-throughput manner. RESULTS We show that our model predictions are reproducible in external datasets generated in similar, but not identical conditions, and have demonstrated the advantage of our approach over the common practice of fixed mass measurement error limits. CONCLUSIONS Outlined is an approach which can promote a more rational use of MS technology by automatically evaluating the absolute mass measurement error based on the individual peak conditions. The immediate application of our method is integration in high-throughput peak annotation pipelines for database searches
Data integration for decision making in wheat breeding
No full textPlant breeding is a production process requiring the creation of germplasm through taking existing successful cultivars and crossing them with new parental lines with agronomic and quality attributes of interest. After crossing, F2 generations generally display all possible combinations between the parental lines. The process from this step is to identify elite crossbred lines and backcross these several times to the parental lines in order to generate new elite lines that are predominately equivalent to the cultivar but with specific novel and desirable attributes present.
Plant breeding continually requires judgements to identify elite plant germplasm containing traits that maximise plant performance. These judgements are often made using incomplete information resulting from the greater complexity in modern plant breeding decision making. Judgements can be improved through the utilisation of new technologies and a stronger scientific basis. This thesis uses decision and information management processes to contribute to:
• Pioneering the application of unbalanced datasets to wheat breeding. The methodologies were derived from tree and animal breeding experience and successfully applied to data sets from a wheat breeding program.
• Providing the first integration of molecular data into a decision-matrix framework.
• Building on the molecular integration in output-2 by establishing a more sophisticated integration of complex NIR spectral data with molecular data.
• Providing inputs into decision matrices for breeding using the outputs discussed above.
This thesis establishes the methodology to make use of new technologies to use unbalanced datasets with decision matrix methodology to make better decisions. This thesis has utilised multivariate methodologies more broadly to include complex data such as NIR fingerprint to differentiate flour samples between controls and breeding germplasm. These differences appear to be related to genetic factors as demonstrated after variability relating to the environment had been removed. This thesis first reviews the literature and then addresses this breeding processes through the use of decision and information management processes, and makes significant contributions in using these methodologies
A Plant-inspired Light Transducer for High-performance Near-infrared Light Mediated Gas Sensing
No full textConstructing near-infrared light (NIR) light-enhanced room temperature gas sensors is becoming more promising for practical application. In this study, learning from the structure and photosynthetic process of chlorophyll thylakoid membranes in plants, the first “Thylakoid membrane” structural formaldehyde (HCHO) sensor is constructed by matching the upconversion emission of the lanthanide-doped upconversion nanoparticles (UCNPs) and the UV–vis adsorption of the as-prepared nanocomposites. The NIR-mediated sensor exhibits excellent performances, including ultra-high response (Ra / Rg = 2.22, 1 ppm), low practical limit of detection (50 ppb), reliable repeatability, high selectivity, and broadband spectral response. The practicality of the NIR-mediated gas sensor is confirmed through the remote and external stimulation test. A study of sensing mechanism demonstrates that it is the UCNPs-based light transducer produces more light-induced oxygen species for gas response in the process of non-radiative/radiative energy transfer, playing a key role in significantly improving the sensing properties of the sensor. The universality of NIR-mediated gas sensors based on UCNPs is verified using ZnO, In2O3, and SnO2 systems. This work paves a way for fabricating high-performance NIR-mediated gas sensors and will expand the application fields of NIR light.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Bio-Electronic
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