timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery.

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19 Intensive Care Unit.

Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions.

SignificanceDeep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle-protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of deep, precise, unbiased plasma proteomics at a scale compatible with large-scale genomics enabling multiomic studies.

Analysis of the Human Plasma Proteome Using Multi-Nanoparticle Protein Corona for Detection of Alzheimer's Disease.

As the population affected by Alzheimer's disease (AD) grows, so does the need for a noninvasive and accurate diagnostic tool. Current research reveals that AD pathogenesis begins as early as decades before clinical symptoms. The unique properties of nanoparticles (NPs) may be exploited to develop noninvasive diagnostics for early detection of AD. After exposure of NPs to biological fluids, the NP surface is altered by an unbiased but selective and reproducible adsorption of biomolecules commonly referred to as the biomolecular corona or protein corona (PC). The discovery that the plasma proteome may be differentially altered during health and disease leads to the concept of disease-specific PCs. Herein, the disease-specific PCs formed around NPs in a multi-NPs platform are employed to successfully identify subtle changes in plasma protein patterns and detect AD (>92% specificity and ≈100% sensitivity). Similar discrimination power is achieved using banked plasma samples from a cohort of patients several years prior to their diagnosis with AD. With the nanoplatform's analytic ability to analyze pathological proteomic changes into a disease-specific identifier, this promising, noninvasive technology with implications for early detection and intervention could benefit not only patients with AD but other diseases as well.

Blind source separation with integrated photonics and reduced dimensional statistics.

Microwave communications have witnessed an incipient proliferation of multi-antenna and opportunistic technologies in the wake of an ever-growing demand for spectrum resources, while facing increasingly difficult network management over widespread channel interference and heterogeneous wireless broadcasting. Radio frequency (RF) blind source separation (BSS) is a powerful technique for demixing mixtures of unknown signals with minimal assumptions, but relies on frequency dependent RF electronics and prior knowledge of the target frequency band. We propose photonic BSS with unparalleled frequency agility supported by the tremendous bandwidths of photonic channels and devices. Specifically, our approach adopts an RF photonic front-end to process RF signals at various frequency bands within the same array of integrated microring resonators, and implements a novel two-step photonic BSS pipeline to reconstruct source identities from the reduced dimensional statistics of front-end output. We verify the feasibility and robustness of our approach by performing the first proof-of-concept photonic BSS experiments on mixed-over-the-air RF signals across multiple frequency bands. The proposed technique lays the groundwork for further research in interference cancellation, radio communications, and photonic information processing.

Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona.

Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies.

Photonic independent component analysis using an on-chip microring weight bank.

Independent component analysis (ICA) is a general-purpose technique for analyzing multi-dimensional data to reveal the underlying hidden factors that are maximally independent from each other. We report the first photonic ICA on mixtures of unknown signals by employing an on-chip microring (MRR) weight bank. The MRR weight bank performs so-called weighted addition (i.e., multiply-accumulate) operations on the received mixtures, and outputs a single reduced-dimensional representation of the signal of interest. We propose a novel ICA algorithm to recover independent components solely based on the statistical information of the weighted addition output, while remaining blind to not only the original sources but also the waveform information of the mixtures. We investigate both channel separability and near-far problems, and our two-channel photonic ICA experiment demonstrates our scheme holds comparable performance with the conventional software-based ICA method. Our numerical simulation validates the fidelity of the proposed approach, and studies noise effects to identify the operating regime of our method. The proposed technique could open new domains for future research in blind source separation, microwave photonics, and on-chip information processing.

Accelerated secure key distribution based on localized and asymmetric fiber interferometers.

We propose and experimentally demonstrate an approach to generate and distribute secret keys over optical fiber communication infrastructure. Mach-Zehnder interferometers (MZIs) are adopted for key generation by transferring the environmental noise to random optical signals. A novel combination of wideband optical noise and an asymmetric MZI structure enables the secret keys to be securely transmitted and exchanged over public fiber links without being detected. We experimentally demonstrate this system and show reliable performance: keys are generated at the rate of 502 bit/s, and are successfully exchanged between two parties over a 10 km optical fiber with a bit error of ∼ 0.3%. System security analysis is performed by corroborating our experimental findings with simulations. The results show that our system can protect the key distribution under different attacks, attributed to wideband optical noise and asymmetric MZI structures. Compared to the previous schemes based on distributed MZIs, our scheme exploits localized MZI which provides twofold advantages. Firstly, the key generation rate can be increased by a factor of 5.7 at a negligible additional cost. Secondly, the system becomes robust to, in particular, active intrusion attack. The proposed system is a reliable and cost-effective solution for key establishment, and is compatible with the existing optical fiber communication infrastructure.

Photonic principal component analysis using an on-chip microring weight bank.

Photonic principal component analysis (PCA) enables high-performance dimensionality reduction in wideband analog systems. In this paper, we report a photonic PCA approach using an on-chip microring (MRR) weight bank to perform weighted addition operations on correlated wavelength-division multiplexed (WDM) inputs. We are able to configure the MRR weight bank with record-high accuracy and precision, and generate multi-channel correlated input signals in a controllable manner. We also consider the realistic scenario in which the PCA procedure remains blind to the waveforms of both the input signals and weighted addition output, and propose a novel PCA algorithm that is able to extract principal components (PCs) solely based on the statistical information of the weighted addition output. Our experimental demonstration of two-channel photonic PCA produces PCs holding consistently high correspondence to those computed by a conventional software-based PCA method. Our numerical simulation further validates that our scheme can be generalized to high-dimensional (up to but not limited to eight-channel) PCA with good convergence. The proposed technique could bring new solutions to problems in microwave communications, ultrafast control, and on-chip information processing.

Feedback control for microring weight banks.

Microring weight banks present novel opportunities for reconfigurable, high-performance analog signal processing in photonics. Controlling microring filter response is a challenge due to fabrication variations and thermal sensitivity. Prior work showed continuous weight control of multiple wavelength-division multiplexed signals in a bank of microrings based on calibration and feedforward control. Other prior work has shown resonance locking based on feedback control by monitoring photoabsorption-induced changes in resistance across in-ring photoconductive heaters. In this work, we demonstrate continuous, multi-channel control of a microring weight bank with an effective 5.1 bits of accuracy on 2Gbps signals. Unlike resonance locking, the approach relies on an estimate of filter transmission versus photo-induced resistance changes. We introduce an estimate still capable of providing 4.2 bits of accuracy without any direct transmission measurements. Furthermore, we present a detailed characterization of this response for different values of carrier wavelength offset and power. Feedback weight control renders tractable the weight control problem in reconfigurable analog photonic networks.

Simultaneous excitatory and inhibitory dynamics in an excitable laser.

Neocortical systems encode information in electrochemical spike timings, not just mean firing rates. Learning and memory in networks of spiking neurons is achieved by the precise timing of action potentials that induces synaptic strengthening (with excitation) or weakening (with inhibition). Inhibition should be incorporated into brain-inspired spike processing in the optical domain to enhance its information-processing capability. We demonstrate the simultaneous excitatory and inhibitory dynamics in an excitable (i.e., a pulsed) laser neuron, both numerically and experimentally. We investigate the bias strength effect, inhibitory strength effect, and excitatory and inhibitory input timing effect, based on the simulation platform of an integrated graphene excitable laser. We further corroborate these analyses with proof-of-principle experiments utilizing a fiber-based graphene excitable laser, where we introduce inhibition by directly modulating the gain of the laser. This technology may potentially open novel spike-processing functionality for future neuromorphic photonic systems.

An analysis of messages sent between nurses and physicians in deteriorating internal medicine patients to help identify issues in failures to rescue.

OBJECTIVE: To evaluate in patients who deteriorate and require transfer to the intensive care unit (ICU), how many have a critical text message communicating deterioration and what is the quality of this message? Is message quality, message response or the timeliness of rapid response team (RRT) activation related to death? METHODS: We conducted a retrospective chart review of all ICU transfers from General Internal Medicine (GIM) wards from January 2012 until August 2014. All critical messages (CM) in the 48h prior to ICU transfer were analyzed for RRT calling criteria, time to RRT activation, message quality, presence of vitals, and the quality and timeliness of physician response. RESULTS: Of the 236 patients in the study, 93 (39%) had a CM in the 48h prior to ICU transfer. Within this subset, 76 patients did not have prior RRT activation and the median times from CM to RRT activation and CM to ICU transfer were 8.9 [IQR 2.9, 20.7] and 15.6 [IQR 9.0, 28.7] hours, respectively. Only 45% of messages contained 2 or more vitals and only 3% of messages contained Situation, Background, Assessment, and Recommendations (SBAR). Physician response was timely (3 [IQR 2, 17] min) but response quality was poor; nearly one quarter of responses only acknowledged receipt. Among message characteristics, only the number of SBAR elements was correlated with in-hospital survival (p=0.047). CONCLUSION: Communication between nurses and physicians about critically ill patients could be improved. There appear to be significant gaps in the quality of messages, their responses, and delays in RRT activation.

Gigabit Ethernet signal transmission using asynchronous optical code division multiple access.

We propose and experimentally demonstrate a novel architecture for interfacing and transmitting a Gigabit Ethernet (GbE) signal using asynchronous incoherent optical code division multiple access (OCDMA). This is the first such asynchronous incoherent OCDMA system carrying GbE data being demonstrated to be working among multi-users where each user is operating with an independent clock/data rate and is granted random access to the network. Three major components, the GbE interface, the OCDMA transmitter, and the OCDMA receiver are discussed in detail. The performance of the system is studied and characterized through measuring eye diagrams, bit-error rate and packet loss rate in real-time file transfer. Our Letter also addresses the near-far problem and realizes asynchronous transmission and detection of signal.

The microeconomics of personalized medicine: today's challenge and tomorrow's promise.

'Personalized medicine' promises to increase the quality of clinical care and, in some cases, decrease health-care costs. Despite this, only a handful of diagnostic tests have made it to market, with mixed success. Historically, the challenges in this field were scientific. However, as discussed in this article, with the maturation of the '-omics' sciences, it now seems that the major barriers are increasingly related to economics. Overcoming the poor microeconomic alignment of incentives among key stakeholders is therefore crucial to catalysing the further development and adoption of personalized medicine, and we propose several actions that could help achieve this goal.

What drives success for specialty pharmaceuticals?

Specialty pharmaceuticals have become increasingly important in the global pharmaceutical landscape. Numerous large pharmaceutical companies are moving towards developing therapies for specialty markets, which are attractive owing to factors including the established commercial track record and lower commercial infrastructure costs. In this article, we analyse the key drivers of commercial success and failure for specialty pharmaceuticals.

Synthesis of cyclic and acyclic beta-amino acids via chelation-controlled 1,3-dipolar cycloaddition.

Isoxazolidines have been synthesized in diastereomeric excess up to 94% via a MgBr2-induced chelation-controlled 1,3-dipolar cycloaddition reaction with N-hydroxyphenylglycinol as a chiral auxiliary. The diastereomerically pure isoxazolidines were further transformed into cyclic and acyclic beta-amino acid derivatives.