Interferobot: aligning an optical interferometer by a reinforcement learning agent

Limitations in acquiring training data restrict potential applications of deep reinforcement learning (RL) methods to the training of real-world robots. Here we train an RL agent to align a Mach-Zehnder interferometer, which is an essential part of many optical experiments, based on images of interference fringes acquired by a monocular camera. The agent is trained in a simulated environment, without any hand-coded features or a priori information about the physics, and subsequently transferred to a physical interferometer. Thanks to a set of domain randomizations simulating uncertainties in physical measurements, the agent successfully aligns this interferometer without any fine tuning, achieving a performance level of a human expert

Sideband Injection Locking in Microresonator Frequency Combs

Frequency combs from continuous-wave-driven Kerr-nonlinear microresonators have evolved into a key photonic technology with applications from optical communication to precision spectroscopy. Essential to many of these applications is the control of the comb's defining parameters, i.e., carrier-envelope offset frequency and repetition rate. An elegant and all-optical approach to controlling both degrees of freedom is the suitable injection of a secondary continuous-wave laser into the resonator onto which one of the comb lines locks. Here, we study experimentally such sideband injection locking in microresonator soliton combs across a wide optical bandwidth and derive analytic scaling laws for the locking range and repetition rate control. As an application example, we demonstrate optical frequency division and repetition rate phase-noise reduction to three orders of magnitude below the noise of a free-running system. The presented results can guide the design of sideband injection-locked, parametrically generated frequency combs with opportunities for low-noise microwave generation, compact optical clocks with simplified locking schemes and more generally, all-optically stabilized frequency combs from Kerr-nonlinear resonators

Additional file 1: Figure S1. of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

The pathway for the biosynthesis of polyamines and related metabolites starting from the assimilation of nitrogen (Adapted from Majumdar et al. 2016). Figure S2. (A, B) - Quality control scatter plots showing expression level for data that passed CV and dye-swap tests. Red spots indicate data that passed statistical analysis for differential expression between the control and the HP cells. Figure S3. The loading plots (S-plot) of the OPLS-DA results for the control and HP cell extracts on days 2 (A), 4 (B), and 6 (C). In the S-plot, each point represents a single metabolite (marker). The x-axis shows the variable contributions. The farther away a data point is from the 0 value, the more it contributes to sample variance. The y-axis shows the sample correlations within the same sample group. The farther away a metabolite is from the 0 value, the better is its correlation from injection to injection. As a result, the metabolites on both ends of the S-shaped curve represent the leading contributing ions from each sample group. The OPLS-DA is a multivariate analysis model which separates the systematic variation in X into two parts, one that is linearly related (and therefore predictive) to Y and one that is orthogonal to Y (unrelated); the Y-predictive/related part represents the between-class variation, the Y-orthogonal (ToPo) part constitutes the within-class variation. (DOCX 1653 kb

Additional file 3: Table S1. of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

Summary of changes in expression of genes involved in polyamine metabolism. For abbreviations see Fig. 1. Sequences for AS, CARB, DAO, NAGK, NAGPR, NAOD, NAOGAcT, ODC, OTC and SPDS were not represented on the microarray. Table S2. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on both day 3 and day 5. Table S3. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on day 3 only. Table S4. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on day 5 only. Table S5. List of metabolites that were positively identified in poplar control and HP cell lines. ND = not detectable. Values that are significantly different (P ≤ 0.05) in the HP cells from the corresponding control cells on a given day are marked in bold. (DOCX 103 kb

Supplementary document for Dual-laser self-injection locking to an integrated microresonator - 5791625.pdf

Supplemental Material

Sugar alcohols present in <i>S. aureus</i> strains.

<p>GC-MS was used to analyse cytoplasmic fractions from exponential growth phase cells. 131 unique metabolites were compared and chromatograms and mass spectra were evaluated as described previously <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067698#pone.0067698-Campbell1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067698#pone.0067698-Bremell1" target="_blank">[32]</a> using the MSD ChemStation (Agilent, Palo Alto, CA, USA) and AMDIS (NIST, Gaithersburg, MD, USA) programs. The resulting data from triplicate samples (with less than 10% variability) were analyzed using a t-test. Samples with greater than 2-fold variation (p<0.05) were analyzed using the MetPA enrichment pathway analysis web application (<a href="http://metpa.metabolomics.ca/" target="_blank">http://metpa.metabolomics.ca/</a>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067698#pone.0067698-Chaturvedi1" target="_blank">[45]</a>. ND, not detectable.</p

Additional file 2: of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

Methods for RNA extraction, cDNA preparation and labeling, microarray hybridization and processing, and metabolomic analysis. Supplemental data Microarrays: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79420 . Metabolomic Data: https://mynotebook.labarchives.com/share/ulav72/MjIuMXwxNzEzMTkvMTcvVHJlZU5vZGUvMzg1Mzg2MTkxNHw1Ni4x . (PDF 451 kb

3d_theory.fig

The 3D figure of dual-laser self-injection locking surface with the capability of drag and rotation. The figure can be opened by Matlab

Mtl fermentation capability of <i>S. aureus</i> strains.

<p>Mtl fermentation is revealed by acid formation and colour change of the pH indicator to yellow. Liv1023 (SH1000 <i>mtlD</i>::<i>tet</i>) and Liv1024 (SH1000 <i>mtlABFD</i>::<i>tet</i>) do not ferment Mtl and this capability was restored by complementation with the entire locus in strains Liv1098 (SH1000 <i>mtlD</i>::<i>tet</i> pMJH71) and Liv1097 (SH1000 <i>mtlABFD</i>::<i>tet</i> pMJH71). Weak growth of Liv1023 was observed.</p

Mannitol Utilisation is Required for Protection of <i>Staphylococcus aureus</i> from Human Skin Antimicrobial Fatty Acids

<div><p>Mannitol (Mtl) fermentation, with the subsequent production of acid, is a species signature of <i>Staphylococcus aureus</i>, and discriminates it from most other members of the genus. Inactivation of the gene <i>mtlD,</i> encoding Mtl-1-P dehydrogenase was found to markedly reduce survival in the presence of the antimicrobial fatty acid, linoleic acid. We demonstrate that the sugar alcohol has a potentiating action for this membrane-acting antimicrobial. Analysis of cellular metabolites revealed that, during exponential growth, the <i>mtlD</i> mutant accumulated high levels of Mtl and Mtl-P. The latter metabolite was not detected in its isogenic parent strain or a deletion mutant of the entire <i>mtlABFD</i> operon. In addition, the <i>mtlD</i> mutant strain exhibited a decreased MIC for H₂O₂, however virulence was unaffected in a model of septic arthritis.</p></div