Interferometric Imaging Using Shared Quantum Entanglement.

Quantum entanglement-based imaging promises significantly increased resolution by extending the spatial separation of optical collection apertures used in very-long-baseline interferometry for astronomy and geodesy. We report a tabletop entanglement-based interferometric imaging technique that utilizes two entangled field modes serving as a phase reference between two apertures. The spatial distribution of a simulated thermal light source is determined by interfering light collected at each aperture with one of the entangled fields and performing joint measurements. This experiment demonstrates the ability of entanglement to implement interferometric imaging.

Quantifying the enhancement of two-photon absorption due to spectral-temporal entanglement.

When a low flux of time-frequency-entangled photon pairs (EPP) illuminates a two-photon transition, the rate of two-photon absorption (TPA) can be enhanced considerably by the quantum nature of photon number correlations and frequency correlations. We use a quantum-theoretic derivation of entangled TPA (ETPA) and calculate an upper bound on the amount of quantum enhancement that is possible in such systems. The derived bounds indicate that in order to observe ETPA the experiments would need to operate at a combination of significantly higher rates of EPP illumination, molecular concentrations, and conventional TPA cross sections than are achieved in typical experiments.

Entangled two-photon absorption by atoms and molecules: A quantum optics tutorial.

Two-photon absorption (TPA) and other nonlinear interactions of molecules with time-frequency-entangled photon pairs have been predicted to display a variety of fascinating effects. Therefore, their potential use in practical quantum-enhanced molecular spectroscopy requires close examination. This Tutorial presents a detailed theoretical study of one- and two-photon absorption by molecules, focusing on how to treat the quantum nature of light. We review some basic quantum optics theory and then we review the density-matrix (Liouville) derivation of molecular optical response, emphasizing how to incorporate quantum states of light into the treatment. For illustration, we treat in detail the TPA of photon pairs created by spontaneous parametric down conversion, with an emphasis on how quantum light TPA differs from that with classical light. In particular, we treat the question of how much enhancement of the TPA rate can be achieved using entangled states. This Tutorial includes a review of known theoretical methods and results as well as some extensions, especially the comparison of TPA processes that occur via far-off-resonant intermediate states only and those that involve off-resonant intermediate states by virtue of dephasing processes. A brief discussion of the main challenges facing experimental studies of entangled two-photon absorption is also given.

Time-frequency optical filtering: efficiency vs. temporal-mode discrimination in incoherent and coherent implementations.

Time-frequency (TF) filtering of analog signals has played a crucial role in the development of radio-frequency communications and is currently being recognized as an essential capability for communications, both classical and quantum, in the optical frequency domain. How best to design optical time-frequency (TF) filters to pass a targeted temporal mode (TM), and to reject background (noise) photons in the TF detection window? The solution for 'coherent' TF filtering is known-the quantum pulse gate-whereas the conventional, more common method is implemented by a sequence of incoherent spectral filtering and temporal gating operations. To compare these two methods, we derive a general formalism for two-stage incoherent time-frequency filtering, finding expressions for signal pulse transmission efficiency, and for the ability to discriminate TMs, which allows the blocking of unwanted background light. We derive the tradeoff between efficiency and TM discrimination ability, and find a remarkably concise relation between these two quantities and the time-bandwidth product of the combined filters. We apply the formalism to two examples-rectangular filters or Gaussian filters-both of which have known orthogonal-function decompositions. The formalism can be applied to any state of light occupying the input temporal mode, e.g., 'classical' coherent-state signals or pulsed single-photon states of light. In contrast to the radio-frequency domain, where coherent detection is standard and one can use coherent matched filtering to reject noise, in the optical domain direct detection is optimal in a number of scenarios where the signal flux is extremely small. Our analysis shows how the insertion loss and SNR change when one uses incoherent optical filters to reject background noise, followed by direct detection, e.g. photon counting. We point out implications in classical and quantum optical communications. As an example, we study quantum key distribution, wherein strong rejection of background noise is necessary to maintain a high quality of entanglement, while high signal transmission is needed to ensure a useful key generation rate.

Fluorescence-detected Fourier transform electronic spectroscopy by phase-tagged photon counting.

Fluorescence-detected Fourier transform (FT) spectroscopy is a technique in which the relative paths of an optical interferometer are controlled to excite a material sample, and the ensuing fluorescence is detected as a function of the interferometer path delay and relative phase. A common approach to enhance the signal-to-noise ratio in these experiments is to apply a continuous phase sweep to the relative optical path, and to detect the resulting modulated fluorescence using a phase-sensitive lock-in amplifier. In many important situations, the fluorescence signal is too weak to be measured using a lock-in amplifier, so that photon counting techniques are preferred. Here we introduce an approach to low-signal fluorescence-detected FT spectroscopy, in which individual photon counts are assigned to a modulated interferometer phase ('phase-tagged photon counting,' or PTPC), and the resulting data are processed to construct optical spectra. We studied the fluorescence signals of a molecular sample excited resonantly by a pulsed coherent laser over a range of photon flux and visibility levels. We compare the performance of PTPC to standard lock-in detection methods and establish the range of signal parameters over which meaningful measurements can be carried out. We find that PTPC generally outperforms the lock-in detection method, with the dominant source of measurement uncertainty being associated with the statistics of the finite number of samples of the photon detection rate.

Photonic temporal-mode multiplexing by quantum frequency conversion in a dichroic-finesse cavity.

Photonic temporal modes (TMs) form a field-orthogonal basis set representing a continuous-variable degree of freedom that is in principle infinite dimensional, and create a promising resource for quantum information science and technology. The ideal quantum pulse gate (QPG) is a device that multiplexes and demultiplexes temporally orthogonal optical pulses that have the same carrier frequency, spatial mode, and polarization. The QPG is the chief enabling technology for usage of orthogonal temporal modes as a basis for high-dimensional quantum information storage and processing. The greatest hurdle for QPG implementation using nonlinear-optical, parametric processes with time-varying pump or control fields is the limitation on achievable temporal mode selectivity, defined as perfect TM discrimination combined with unity efficiency. We propose the use of pulsed nonlinear frequency conversion in an optical cavity having greatly different finesses for different frequencies to implement a nearly perfectly TM-selective QPG in a low-loss integrated-optics platform.

Temperature-dependent conformations of exciton-coupled Cy3 dimers in double-stranded DNA.

Understanding the properties of electronically interacting molecular chromophores, which involve internally coupled electronic-vibrational motions, is important to the spectroscopy of many biologically relevant systems. Here we apply linear absorption, circular dichroism, and two-dimensional fluorescence spectroscopy to study the polarized collective excitations of excitonically coupled cyanine dimers (Cy3)2 that are rigidly positioned within the opposing sugar-phosphate backbones of the double-stranded region of a double-stranded (ds)-single-stranded (ss) DNA fork construct. We show that the exciton-coupling strength of the (Cy3)2-DNA construct can be systematically varied with temperature below the ds-ss DNA denaturation transition. We interpret spectroscopic measurements in terms of the Holstein vibronic dimer model, from which we obtain information about the local conformation of the (Cy3)2 dimer, as well as the degree of static disorder experienced by the Cy3 monomer and the (Cy3)2 dimer probe locally within their respective DNA duplex environments. The properties of the (Cy3)2-DNA construct we determine suggest that it may be employed as a useful model system to test fundamental concepts of protein-DNA interactions and the role of electronic-vibrational coherence in electronic energy migration within exciton-coupled bio-molecular arrays.

Engineering temporal-mode-selective frequency conversion in nonlinear optical waveguides: from theory to experiment.

Quantum frequency conversion (FC) in nonlinear optical media is a powerful tool for temporal-mode selective manipulation of light. Recent attempts at achieving high mode selectivities and/or fidelities have had to resort to multi-dimensional optimization schemes to determine the system's natural Schmidt modes. Certain combinations of relative-group velocities between the relevant frequency bands, medium length, and temporal pulse widths have been known to achieve good selectivities (exceeding 80%) for temporal modes that are nearly identical to pump pulse shapes, even for high conversion efficiencies. Working in this parameter regime using an off-the-shelf, second-harmonic generation, MgO:PPLN waveguide, and with pulses on the order of 500 fs at wavelengths around 800 nm, we verify experimentally that model-predicted Schmidt modes provide the high temporal-mode selectivity expected. The good agreement between experiment and theory paves the way to the implementation of a proposed two-stage FC scheme that is predicted by the present theory to reach near-perfect (100%) selectivity.

Observation of Interaction of Spin and Intrinsic Orbital Angular Momentum of Light.

The interaction of spin and intrinsic orbital angular momentum of light is observed, as evidenced by length-dependent rotations of both spatial patterns and optical polarization in a cylindrically symmetric isotropic optical fiber. Such rotations occur in a straight few-mode fiber when superpositions of two modes with parallel and antiparallel orientation of spin and intrinsic orbital angular momentum (IOAM=2ℏ) are excited, resulting from a degeneracy splitting of the propagation constants of the modes.

Metabolic and translational efficiency in microbial organisms.

Metabolic efficiency, as a selective force shaping proteomes, has been shown to exist in Escherichia coli and Bacillus subtilis and in a small number of organisms with photoautotrophic and thermophilic lifestyles. Earlier attempts at larger-scale analyses have utilized proxies (such as molecular weight) for biosynthetic cost, and did not consider lifestyle or auxotrophy. This study extends the analysis to all currently sequenced microbial organisms that are amenable to these analyses while utilizing lifestyle specific amino acid biosynthesis pathways (where possible) to determine protein production costs and compensating for auxotrophy. The tendency for highly expressed proteins (with adherence to codon usage bias as a proxy for expressivity) to utilize less biosynthetically expensive amino acids is taken as evidence of cost selection. A comprehensive analysis of sequenced genomes to identify those that exhibit strong translational efficiency bias (389 out of 1,700 sequenced organisms) is also presented.

Amino acid biosynthetic cost and protein conservation.

Protein products of highly expressed genes tend to favor amino acids that have lower average biosynthetic costs (i.e., they exhibit metabolic efficiency). While this trend has been observed in several studies, the specific sites where cost-reducing substitutions accumulate have not been well characterized. Toward that end, weighted costs in conserved and variable positions were evaluated across a total of 9,119 homologous proteins in four mammalian orders (primate, carnivore, rodent, and artiodactyls), which together contain a total of 20,457,072 amino acids. Degree of conservation at homologous positions in these mammalian proteins and average-weighted cost across all positions within a single protein are significantly correlated. Dividing human genes into two classes (those with and those without CpG islands in their promoters) suggests that humans also preferentially utilize less costly amino acids in highly expressed genes. In contrast to the intuitive expectation that the relatively weak selective force associated with metabolic efficiency would be a selection pressure in complex multicellular organisms, the overall level of selective constraint within the variable regions of mammalian proteins allows the metabolic efficiency to derive a reduction of overall biosynthetic cost, particularly in genes with the highest levels of expression.

Multi-label classification and label dependence in in silico toxicity prediction.

Most computational predictive models are specifically trained for a single toxicity endpoint and lack the ability to learn dependencies between endpoints, such as those targeting similar biological pathways. In this study, we compare the performance of 3 multi-label classification (MLC) models, namely Classifier Chains (CC), Label Powersets (LP) and Stacking (SBR), against independent classifiers (Binary Relevance) on Tox21 challenge data. Also, we develop a novel label dependence measure that shows full range of values, even at low prior probabilities, for the purpose of data-driven label partitioning. Using Logistic Regression as the base classifier and random label partitioning (k = 3), CC show statistically significant improvements in model performance using Hamming and multi-label accuracy scores (p<0.05), while SBR show significant improvements in multi-label accuracy scores. The weights in the Logistic Regression and Stacking models are positively associated with label dependencies, suggesting that learning label dependence is a key contributor to improving model performance. An original quantitative measure of label dependency is combined with the Louvain community detection method to learn label partitioning using a data-driven process. The resulting MLCs with learned label partitioning were generally found to be non-inferior to their corresponding random or no label partitioning counterparts. Additionally, using the Random Forest classifier in a 10-fold stratified cross validation Stacking model, we find that the top-performing stacking model out-performs the corresponding base model in 11 out of 12 Tox21 labels. Taken together, these results suggest that MLC models could potentially boost the performance of current single-endpoint predictive models and that label partitioning learning may be used in place of random label partitionings.

Urinary Metabolites as Predictors of Acute Mountain Sickness Severity.

Individuals sojourning at high altitude (≥2,500m) often develop acute mountain sickness (AMS). However, substantial unexplained inter-individual variability in AMS severity exists. Untargeted metabolomics assays are increasingly used to identify novel biomarkers of susceptibility to illness, and to elucidate biological pathways linking environmental exposures to health outcomes. This study used untargeted nuclear magnetic resonance (NMR)-based metabolomics to identify urine metabolites associated with AMS severity during high altitude sojourn. Following a 21-day stay at sea level (SL; 55m), 17 healthy males were transported to high altitude (HA; 4,300m) for a 22-day sojourn. AMS symptoms measured twice daily during the first 5days at HA were used to dichotomize participants according to AMS severity: moderate/severe AMS (AMS; n=11) or no/mild AMS (NoAMS; n=6). Urine samples collected on SL day 12 and HA days 1 and 18 were analyzed using proton NMR tools and the data were subjected to multivariate analyses. The SL urinary metabolite profiles were significantly different (p≤0.05) between AMS vs. NoAMS individuals prior to high altitude exposure. Differentially expressed metabolites included elevated levels of creatine and acetylcarnitine, and decreased levels of hypoxanthine and taurine in the AMS vs. NoAMS group. In addition, the levels of two amino acid derivatives (4-hydroxyphenylpyruvate and N-methylhistidine) and two unidentified metabolites (doublet peaks at 3.33ppm and a singlet at 8.20ppm) were significantly different between groups at SL. By HA day 18, the differences in urinary metabolites between AMS and NoAMS participants had largely resolved. Pathway analysis of these differentially expressed metabolites indicated that they directly or indirectly play a role in energy metabolism. These observations suggest that alterations in energy metabolism before high altitude exposure may contribute to AMS susceptibility at altitude. If validated in larger cohorts, these markers could inform development of a non-invasive assay to screen individuals for AMS susceptibility prior to high altitude sojourn.

A community effort to identify and correct mislabeled samples in proteogenomic studies.

Sample mislabeling or misannotation has been a long-standing problem in scientific research, particularly prevalent in large-scale, multi-omic studies due to the complexity of multi-omic workflows. There exists an urgent need for implementing quality controls to automatically screen for and correct sample mislabels or misannotations in multi-omic studies. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies. The challenge received a large number of submissions from domestic and international data scientists, with highly variable performance observed across the submitted methods. Post-challenge collaboration between the top-performing teams and the challenge organizers has created an open-source software, COSMO, with demonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets.

Characterization of 1H NMR spectroscopic data and the generation of synthetic validation sets.

MOTIVATION: Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are only of value if the data sets are complex enough for results to generalize to true experimental data. Ideally, synthetic data should be indistinguishable from empirical data, yet retain a known best analysis. RESULTS: We have developed a technique for creating realistic synthetic metabolomics validation sets based on NMR spectroscopic data. The validation sets are developed by characterizing the salient distributions in sets of empirical spectroscopic data. Using this technique, several validation sets are constructed with a variety of characteristics present in 'real' data. A case study is then presented to compare the relative accuracy of several alignment algorithms using the increased precision afforded by these synthetic data sets. AVAILABILITY: These data sets are available for download at http://birg.cs.wright.edu/nmr_synthetic_data_sets.

Identification of novel ΔNp63α-regulated miRNAs using an optimized small RNA-Seq analysis pipeline.

Advances in high-throughput sequencing have enabled profiling of microRNAs (miRNAs), however, a consensus pipeline for sequencing of small RNAs has not been established. We built and optimized an analysis pipeline using Partek Flow, circumventing the need for analyzing data via scripting languages. Our analysis assessed the effect of alignment reference, normalization method, and statistical model choice on biological data. The pipeline was evaluated using sequencing data from HaCaT cells transfected with either a non-silencing control or siRNA against ΔNp63α, a p53 family member protein which is highly expressed in non-melanoma skin cancer and shown to regulate a number of miRNAs. We posit that 1) alignment and quantification to the miRBase reference provides the most robust quantitation of miRNAs, 2) normalizing sample reads via Trimmed Mean of M-values is the most robust method for accurate downstream analyses, and 3) use of the lognormal with shrinkage statistical model effectively identifies differentially expressed miRNAs. Using our pipeline, we identified previously unrecognized regulation of miRs-149-5p, 18a-5p, 19b-1-5p, 20a-5p, 590-5p, 744-5p and 93-5p by ΔNp63α. Regulation of these miRNAs was validated by RT-qPCR, substantiating our small RNA-Seq pipeline. Further analysis of these miRNAs may provide insight into ΔNp63α's role in cancer progression. By defining the optimal alignment reference, normalization method, and statistical model for analysis of miRNA sequencing data, we have established an analysis pipeline that may be carried out in Partek Flow or at the command line. In this manner, our pipeline circumvents some of the major hurdles encountered during small RNA-Seq analysis.

Four-wave-mixing cascades near the zero-dispersion frequency.

Exact formulas are obtained for the amplitudes of light waves involved in four-wave-mixing cascades near the zero-dispersion frequency of a fiber. The cascade that is initiated by two strong pump waves is phase insensitive, whereas the cascade that is initiated by two strong pump waves and a weak signal wave is phase sensitive. In both cascades, the number of waves that have significant power increases with distance.

Empirical analysis of the STR profiles resulting from conceptual mixtures.

Samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors can be challenging and associated uncertainties can have dramatic effects on the interpretation of testing results. Using an FBI genotypes dataset, containing complete genotype information from the 13 Combined DNA Index System (CODIS) loci for 959 individuals, all possible mixtures of three individuals were exhaustively and empirically computed. Allele sharing between pairs of individuals in the original dataset, a randomized dataset and datasets of generated cousins and siblings was evaluated as were the number of loci that were necessary to reliably deduce the number of contributors present in simulated mixtures of four or less contributors. The relatively small number of alleles detectable at most CODIS loci and the fact that some alleles are likely to be shared between individuals within a population can make the maximum number of different alleles observed at any tested loci an unreliable indicator of the maximum number of contributors to a mixed DNA sample. This analysis does not use other data available from the electropherograms (such as peak height or peak area) to estimate the number of contributors to each mixture. As a result, the study represents a worst case analysis of mixture characterization. Within this dataset, approximately 3% of three-person mixtures would be mischaracterized as two-person mixtures and more than 70% of four-person mixtures would be mischaracterized as two- or three-person mixtures using only the maximum number of alleles observed at any tested locus.

The networks of human gut microbe-metabolite associations are different between health and irritable bowel syndrome.

The goal of this study was to determine if fecal metabolite and microbiota profiles can serve as biomarkers of human intestinal diseases, and to uncover possible gut microbe-metabolite associations. We employed proton nuclear magnetic resonance to measure fecal metabolites of healthy children and those diagnosed with diarrhea-predominant irritable bowel syndrome (IBS-D). Metabolite levels were associated with fecal microbial abundances. Using several ordination techniques, healthy and irritable bowel syndrome (IBS) samples could be distinguished based on the metabolite profiles of fecal samples, and such partitioning was congruent with the microbiota-based sample separation. Measurements of individual metabolites indicated that the intestinal environment in IBS-D was characterized by increased proteolysis, incomplete anaerobic fermentation and possible change in methane production. By correlating metabolite levels with abundances of microbial genera, a number of statistically significant metabolite-genus associations were detected in stools of healthy children. No such associations were evident for IBS children. This finding complemented the previously observed reduction in the number of microbe-microbe associations in the distal gut of the same cohort of IBS-D children.