Big Data in Laboratory Medicine—FAIR Quality for AI?

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research

The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application.

BACKGROUND Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. OBJECTIVE This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. METHODS A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. RESULTS The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. CONCLUSIONS With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine

Highly Efficient Grating Coupler for Silicon Nitride Photonics with Large Fabrication Tolerance

We demonstrate low-loss fiber-to-chip coupling via a-Si grating couplers on top of SiN waveguides for C-band TE light. The suggested simple scheme is fabrication tolerant and offers a path towards coupling efficiencies above -1 dB

Is There an Ideal Plasmonic Modulator Configuration?

Resonant and non-resonant modulator configurations are compared for operation with the lowest drive voltage. The ring-assisted Mach-Zehnder modulator is shown to offer a steep slope in the transfer function while delivering an open eye diagram. This enables 220GBd 2PAM plasmonic modulation with record low 0.5Vp

216 GBd Plasmonic Ferroelectric Modulator Monolithically Integrated on Silicon Nitride

We demonstrate a 216 GBd plasmonic ferroelectric modulator monolithically integrated with a foundry-produced silicon nitride platform. The combination of low-loss waveguiding, nanoscale plasmonics, and strong Pockels coefficients in barium titanate offers a platform for next-generation optical interconnect systems

Plasmonic Ferroelectric Modulator Monolithically Integrated on SiN for 216 GBd Data Transmission

A high-speed plasmonic barium titanate (BTO, BaTiO3) Mach-Zehnder modulator is presented. We combine nanoscale plasmonics with BTO as solid-state active material and silicon nitride (SiN) for versatile and low loss waveguiding, and integrate them in a monolithic platform. We demonstrate a plasmonic BTO modulator processed onto foundry-produced SiN. The 15 μm long high-speed modulator features a flat electro-optic frequency response up to 70 GHz and is expected to be flat way beyond. A low VπL product of 144 Vμm is shown. Data experiments reaching 216 Gbit/s with a 216 GBd 2PAM signal and 256 Gbit/s with a 128 GBd 4PAM signal are demonstrated. The merger of the versatile silicon nitride platform with high-speed plasmonics using the highly nonlinear ferroelectric BTO is an attractive solution as a future Tb/s optical interconnect platform.ISSN:0733-8724ISSN:1558-221

PLD Epitaxial Thin‐Film BaTiO₃ on MgO − Dielectric and Electro‐Optic Properties

The study demonstrates high-quality pulsed-laser-deposited (PLD) barium titanate (BTO) thin-films on a magnesium oxide substrate. The frequency response of the relative permittivity (dielectric constant) and the linear electro-optical coefficient (Pockels coefficient) are measured. At 0.2 GHz, the Pockels coefficient is fitted to be r42 ≈ 1030 pm V−1. It decreases to ≈390 pm V−1 at 10 GHz after which it remains constant up to 70 GHz. The unbiased BTO permittivity is measured to be εa ≈ 7600 at 0.2 GHz, dropping to ≈1100 at 67 GHz, while the biased BTO had a permittivity εa ≈ 2000 at 0.2 GHz, dropping to ≈500 at 67 GHz. These results fill an important experimental characterization gap for high-speed BTO applications and show the high quality of PLD-grown BTO films. Lastly, the material's crystalline quality is characterized and the domain distribution is imaged. The findings enable the design and fabrication of a new generation of BTO-based components for sensing and communications.ISSN:2196-735