Project description:NMR spectroscopists are hindered by the lack of standardization for spectral data among the file formats for various NMR data processing tools. This lack of standardization is cumbersome as researchers must perform their own file conversion in order to switch between processing tools and also restricts the combination of tools employed if no conversion option is available. The CONNJUR Spectrum Translator introduces a new, extensible architecture for spectrum translation and introduces two key algorithmic improvements. This first is translation of NMR spectral data (time and frequency domain) to a single in-memory data model to allow addition of new file formats with two converter modules, a reader and a writer, instead of writing a separate converter to each existing format. Secondly, the use of layout descriptors allows a single fid data translation engine to be used for all formats. For the end user, sophisticated metadata readers allow conversion of the majority of files with minimum user configuration. The open source code is freely available at http://connjur.sourceforge.net for inspection and extension.
Project description:The dual threats posed by the COVID-19 pandemic and hospital-acquired infections (HAIs) have emphasized the urgent need for self-disinfecting materials for infection control. Despite their highly potent antimicrobial activity, the adoption of photoactive materials to reduce infection transmission in hospitals and related healthcare facilities has been severely hampered by the lack of scalable and cost-effective manufacturing, in which case high-volume production methods for fabricating aPDI-based materials are needed. To address this issue here, we examined the antimicrobial efficacy of a simple bicomponent spray coating composed of the commercially-available UV-photocrosslinkable polymer N-methyl-4(4'-formyl-styryl)pyridinium methosulfate acetal poly(vinyl alcohol) (SbQ-PVA) and one of three aPDI photosensitizers (PSs): zinc-tetra(4-N-methylpyridyl)porphine (ZnTMPyP4+), methylene blue (MB), and Rose Bengal (RB). We applied these photodynamic coatings, collectively termed SbQ-PVA/PS, to a variety of commercially available materials. Scanning electron microscopy (SEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) confirmed the successful application of the coatings, while inductively coupled plasma-optical emission spectroscopy (ICP-OES) revealed a photosensitizer loading of 0.09-0.78 nmol PS/mg material. The antimicrobial efficacy of the coated materials was evaluated against methicillin-susceptible Staphylococcus aureus ATCC-29213 and human coronavirus strain HCoV-229E. Upon illumination with visible light (60 min, 400-700 nm, 65 ± 5 mW/cm2), the coated materials inactivated S. aureus by 97-99.999% and HCoV-229E by 92-99.999%, depending on the material and PS employed. Photobleaching studies employing HCoV-229E demonstrated detection limit inactivation (99.999%) even after exposure for 4 weeks to indoor ambient room lighting. Taken together, these results demonstrate the potential for photodynamic SbQ-PVA/PS coatings to be universally applied to a wide range of materials for effectively reducing pathogen transmission.
Project description:By polling individual responses to hypothetical scenarios, valuation studies estimate population preferences toward health on a quality-adjusted life year (QALY) scale. The scenarios typically involve trade-offs in time (time trade-off (TTO)), risk (standard gamble (SG)), or number of persons affected (person trade-off (PTO)). This paper revisits the QALY assumptions and provides a coherent health econometric approach that unites TTO, SG, and PTO techniques under a common estimator. The proposed approach avoids the use of ratio statistics in QALY estimation and the common convention of arbitrarily changing trade-off responses. As an example, 34% of the TTO responses from the seminal Measurement and Valuation of Health study were changed in the original UK analysis, which led to substantially lower QALY estimates. As a general rule, if the original estimate is less than 0.5 QALYs, add 0.25 QALYs to get the new estimates.
Project description:Despite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguistics. In a two stage process, this paper first evaluates the use of existing lexical measures for the translator stylometry problem. Similar to previous research we found that using vocabulary richness in its traditional form as it has been used in the literature could not identify translator stylometry. This encouraged us to design an approach with the aim of identifying the distinctive patterns of a translator by employing network-motifs. Networks motifs are small sub-graphs which aim at capturing the local structure of a complex network. The proposed approach achieved an average accuracy of 83% in three-way classification. These results demonstrate that classic tools based on lexical features can be used for identifying translator stylometry if they get augmented with appropriate non-parametric scaling. Moreover, the use of complex network analysis and network motifs mining provided made it possible to design features that can solve translator stylometry analysis problems.
Project description:Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.
Project description:ObjectiveThis study aimed to develop a novel, regulatory-compliant approach for openly exposing integrated clinical and environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES).Materials and methodsThe driving clinical use case for research and development of ICEES was asthma, which is a common disease influenced by hundreds of genes and a plethora of environmental exposures, including exposures to airborne pollutants. We developed a pipeline for integrating clinical data on patients with asthma-like conditions with data on environmental exposures derived from multiple public data sources. The data were integrated at the patient and visit level and used to create de-identified, binned, "integrated feature tables," which were then placed behind an OpenAPI.ResultsOur preliminary evaluation results demonstrate a relationship between exposure to high levels of particulate matter ≤2.5 µm in diameter (PM2.5) and the frequency of emergency department or inpatient visits for respiratory issues. For example, 16.73% of patients with average daily exposure to PM2.5 >9.62 µg/m3 experienced 2 or more emergency department or inpatient visits for respiratory issues in year 2010 compared with 7.93% of patients with lower exposures (n = 23 093).DiscussionThe results validated our overall approach for openly exposing and sharing integrated clinical and environmental exposures data. We plan to iteratively refine and expand ICEES by including additional years of data, feature variables, and disease cohorts.ConclusionsWe believe that ICEES will serve as a regulatory-compliant model and approach for promoting open access to and sharing of integrated clinical and environmental exposures data.
Project description:Many policies and projects now encourage investigators to share their raw research data with other scientists. Unfortunately, it is difficult to measure the effectiveness of these initiatives because data can be shared in such a variety of mechanisms and locations. We propose a novel approach to finding shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles. Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision. A simpler version of our classifier achieved higher recall (86%), though lower precision (49%). We believe our results demonstrate the feasibility of this approach and hope to inspire further study of dataset retrieval techniques and policy evaluation.
Project description:The original PRIDE Inspector tool was developed as an open source standalone tool to enable the visualization and validation of mass-spectrometry (MS)-based proteomics data before data submission or already publicly available in the Proteomics Identifications (PRIDE) database. The initial implementation of the tool focused on visualizing PRIDE data by supporting the PRIDE XML format and a direct access to private (password protected) and public experiments in PRIDE.The ProteomeXchange (PX) Consortium has been set up to enable a better integration of existing public proteomics repositories, maximizing its benefit to the scientific community through the implementation of standard submission and dissemination pipelines. Within the Consortium, PRIDE is focused on supporting submissions of tandem MS data. The increasing use and popularity of the new Proteomics Standards Initiative (PSI) data standards such as mzIdentML and mzTab, and the diversity of workflows supported by the PX resources, prompted us to design and implement a new suite of algorithms and libraries that would build upon the success of the original PRIDE Inspector and would enable users to visualize and validate PX "complete" submissions. The PRIDE Inspector Toolsuite supports the handling and visualization of different experimental output files, ranging from spectra (mzML, mzXML, and the most popular peak lists formats) and peptide and protein identification results (mzIdentML, PRIDE XML, mzTab) to quantification data (mzTab, PRIDE XML), using a modular and extensible set of open-source, cross-platform libraries. We believe that the PRIDE Inspector Toolsuite represents a milestone in the visualization and quality assessment of proteomics data. It is freely available at http://github.com/PRIDE-Toolsuite/.
Project description:A new polyester poly(glycerol butenedioate) (PGB) was obtained in the bulk polycondensation of glycerin and maleic anhydride. Glycerol polyesters are new biomaterials commonly used in tissue engineering. PGB, containing the α,β-unsaturated moiety, could be very interesting due to potential modifications such as additions or oxidation. Such modifications are not possible on the heretofore known glycerol polyesters due to their structure without α,β-unsaturated moieties. In this work, the developed process was optimized by applying the design of experiments. The optimization criterium was the minimization of the E/Z isomer ratio. Applying the two-stage process, the E/Z isomer ratio was reduced from 5.5 to 0.5 compared to the one-stage process. The degree of branching was also reduced from 17 to 9%, as well as the degree of esterification from 0.89 to 0.72. The obtained structure can be used in modifying or cross-linking via Michael additions.