Engineering spectrally unentangled photon pairs from nonlinear microring resonators through pump manipulation

The future of integrated quantum photonics relies heavily on the ability to engineer refined methods for preparing the quantum states needed to implement various quantum protocols. An important example of such states are quantum-correlated photon pairs, which can be efficiently generated using spontaneous nonlinear processes in integrated microring-resonator structures. In this work, we propose a method for generating spectrally unentangled photon pairs from a standard microring resonator. The method utilizes interference between a primary and a delayed secondary pump pulse to effectively increase the pump spectral width inside the cavity. This enables on-chip generation of heralded single photons with state purities in excess of 99 % without spectral filtering.Comment: 5 pages, 5 figure

Shape-preserving and unidirectional frequency conversion using four-wave mixing Bragg scattering

In this work, we investigate the properties of four-wave mixing Bragg scattering in a configuration that employs orthogonally polarized pumps in a birefringent waveguide. This configuration enables a large signal conversion bandwidth, and allows strongly unidirectional frequency conversion as undesired Bragg-scattering processes are suppressed by waveguide birefringence. Moreover, we show that this form of four-wave mixing Bragg scattering preserves the (arbitrary) signal pulse shape, even when driven by pulsed pumps.Comment: 11 pages + refs, 5 figure

Gait Mechanics are Influenced by Quadriceps Strength, Age, and Sex after Total Knee Arthroplasty

Although most patients are satisfied with outcomes after total knee arthroplasty (TKA), many retain preoperative altered gait mechanics. Identifying patient characteristics associated with gait mechanics will improve rehabilitation strategies and enhance our understanding of movement disorders. Therefore, the purpose of this study was to identify which patient characteristics are related to gait mechanics in the surgical limb during walking post-TKA. Patient characteristics included age, body mass, sex, quadriceps strength, self-reported function, and knee pain. General linear regression was used to compare patient characteristics associated with gait mechanics, after controlling for gait speed, functional capacity and time from surgery. We tested 191 patients cross-sectionally at 6–24 months after primary, unilateral TKA. Quadriceps weakness in the surgical limb was associated with less peak vertical ground reaction force (PvGRF) (β = .245, p = .044), knee extension moment (β = .283, p = .049), and knee extension excursion (β = .298, p = .038). Older age (β = .168, p = .050) was associated with less PvGRF. Quadriceps strength in the nonsurgical limb (β = −.357, p = .021) was associated with greater knee extension excursion in the surgical limb. Females with TKA (β = −.276, p = .007) had less knee flexion excursion compared to males. Faster gait speed was also associated with greater PvGRF (β = .585, p \u3c .001), knee extensor moment (β = .481, p \u3c .001), and knee flexion excursion (β = .318, p \u3c .001). Statement of Clinical Significance: This study showed quadriceps weakness, slower gait speed, older age and being female were related to altered gait mechanics post-TKA. These findings will help clinicians better educate patients and develop targeted interventions for improving care in patients post-TKA

Morphological Instabilities in a growing Yeast Colony: Experiment and Theory

We study the growth of colonies of the yeast Pichia membranaefaciens on agarose film. The growth conditions are controlled in a setup where nutrients are supplied through an agarose film suspended over a solution of nutrients. As the thickness of the agarose film is varied, the morphology of the front of the colony changes. The growth of the front is modeled by coupling it to a diffusive field of inhibitory metabolites. Qualitative agreement with experiments suggests that such a coupling is responsible for the observed instability of the front.Comment: RevTex, 4 pages and 3 figure

Stories vs. facts: triggering emotion and action-taking on climate change

Publisher's version (útgefin grein)Climate change is an issue which elicits low engagement, even among concerned segments of the public. While research suggests that the presentation of factual information (e.g., scientific consensus) can be persuasive to some audiences, there is also empirical evidence indicating that it may also increase resistance in others. In this research, we investigate whether climate change narratives structured as stories are better than informational narratives at promoting pro-environmental behavior in diverse audiences. We propose that narratives structured as stories facilitate experiential processing, heightening affective engagement and emotional arousal, which serve as an impetus for action-taking. Across three studies, we manipulate the structure of climate change communications to investigate how this influences narrative transportation, measures of autonomic reactivity indicative of emotional arousal, and pro-environmental behavior. We find that stories are more effective than informational narratives at promoting pro-environmental behavior (studies 1 and 3) and self-reported narrative transportation (study 2), particularly those with negatively valenced endings (study 3). The results of study 3 indicate that embedding information in story structure influences cardiac activity, and subsequently, pro-environmental behavior. These findings connect works from the fields of psychology, neuroscience, narratology, and climate change communication, advancing our understanding of how narrative structure influences engagement with climate change through emotional arousal, which likely incites pro-environmental behavior as the brain's way of optimizing bodily budgets.This research has been supported by seed funding from the Interacting Minds Centre, Aarhus University, as well as the Aarhus University Research Foundation."Peer Reviewed

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions.

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].This research was funded by the United States Department of Agriculture (USDA), Agriculture Research Service (ARS) under agreement no. 8050-51000-107-000D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA.S

Solar-like oscillations in the G2 subgiant beta Hydri from dual-site observations

We have observed oscillations in the nearby G2 subgiant star beta Hyi using high-precision velocity observations obtained over more than a week with the HARPS and UCLES spectrographs. The oscillation frequencies show a regular comb structure, as expected for solar-like oscillations, but with several l=1 modes being strongly affected by avoided crossings. The data, combined with those we obtained five years earlier, allow us to identify 28 oscillation modes. By scaling the large frequency separation from the Sun, we measure the mean density of beta Hyi to an accuracy of 0.6%. The amplitudes of the oscillations are about 2.5 times solar and the mode lifetime is 2.3 d. A detailed comparison of the mixed l=1 modes with theoretical models should allow a precise estimate of the age of the star.Comment: 13 pages, 14 figures, accepted by ApJ. Fixed minor typo (ref to Fig 14