Project description:Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.
Project description:Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas. We introduce a novel self-supervised modeling approach for population activity in which the model alternates between masking out and reconstructing neural activity across different time steps, neurons, and brain regions. To evaluate our approach, we design unsupervised and supervised prediction tasks using the International Brain Laboratory repeated site dataset, which is comprised of Neuropixels recordings targeting the same brain locations across 48 animals and experimental sessions. The prediction tasks include single-neuron and region-level activity prediction, forward prediction, and behavior decoding. We demonstrate that our multi-task-masking (MtM) approach significantly improves the performance of current state-of-the-art population models and enables multi-task learning. We also show that by training on multiple animals, we can improve the generalization ability of the model to unseen animals, paving the way for a foundation model of the brain at single-cell, single-spike resolution. Project page and code: https://ibl-mtm.github.io/.
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:Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
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.