Prototype of running clinical trials in an untrustworthy environment using blockchain.

Monitoring and ensuring the integrity of data within the clinical trial process is currently not always feasible with the current research system. We propose a blockchain-based system to make data collected in the clinical trial process immutable, traceable, and potentially more trustworthy. We use raw data from a real completed clinical trial, simulate the trial onto a proof of concept web portal service, and test its resilience to data tampering. We also assess its prospects to provide a traceable and useful audit trail of trial data for regulators, and a flexible service for all members within the clinical trials network. We also improve the way adverse events are currently reported. In conclusion, we advocate that this service could offer an improvement in clinical trial data management, and could bolster trust in the clinical research process and the ease at which regulators can oversee trials

Robust prediction of clinical outcomes using cytometry data.

MotivationFlow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction.ResultsWe propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms.Availability and implementationCytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads.Supplementary informationSupplementary data are available at Bioinformatics online

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine

Secondary terms in the number of vanishings of quadratic twists of elliptic curve L-functions

We examine the number of vanishings of quadratic twists of the L-function associated to an elliptic curve. Applying a conjecture for the full asymptotics of the moments of critical L-values we obtain a conjecture for the first two terms in the ratio of the number of vanishings of twists sorted according to arithmetic progressions.Comment: 16 pages, many figure

Wavelength conversion by cavity-enhanced injection-locked four-wavemixing in a fiber-Bragg-grating coupled diode laser

Four-wave mixing (FWM) in a fiber-Bragg-grating (FBG) coupled semiconductor laser is investigated. We show that a large resonance enhancement of the FWM conversion efficiency can be obtained when the laser cavity is injection-locked by the converted signal, and apply this technique to the wavelength conversion of 1-Gb/s modulated signals. Furthermore, we discuss how the spectral width of these resonances can be increased to make this approach suitable to higher bit rates