Single-mode instability in standing-wave lasers: The quantum cascade laser as a self-pumped parametric oscillator

We report the observation of a clear single-mode instability threshold in continuous-wave Fabry-Perot quantum cascade lasers (QCLs). The instability is characterized by the appearance of sidebands separated by tens of free spectral ranges (FSR) from the first lasing mode, at a pump current not much higher than the lasing threshold. As the current is increased, higher-order sidebands appear that preserve the initial spacing, and the spectra are suggestive of harmonically phase-locked waveforms. We present a theory of the instability that applies to all homogeneously broadened standing-wave lasers. The low instability threshold and the large sideband spacing can be explained by the combination of an unclamped, incoherent Lorentzian gain due to the population grating, and a coherent parametric gain caused by temporal population pulsations that changes the spectral gain line shape. The parametric term suppresses the gain of sidebands whose separation is much smaller than the reciprocal gain recovery time, while enhancing the gain of more distant sidebands. The large gain recovery frequency of the QCL compared to the FSR is essential to observe this parametric effect, which is responsible for the multiple-FSR sideband separation. We predict that by tuning the strength of the incoherent gain contribution, for example by engineering the modal overlap factors and the carrier diffusion, both amplitude-modulated (AM) or frequency-modulated emission can be achieved from QCLs. We provide initial evidence of an AM waveform emitted by a QCL with highly asymmetric facet reflectivities, thereby opening a promising route to ultrashort pulse generation in the mid-infrared. Together, the experiments and theory clarify a deep connection between parametric oscillation in optically pumped microresonators and the single-mode instability of lasers, tying together literature from the last 60 years.United States. Defense Advanced Research Projects Agency. Spectral Combs from UV to THz Program (Grant W31P4Q-16-1-0002)National Science Foundation (U.S.) (Awards ECCS-1230477, ECCS-1614631 and ECCS- 1614531)United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering (Air Force Contracts FA8721-05-C- 0002 and No. FA8702-15-D-0001

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

© Copyright © 2020 Sandifer, Knapp, Lichtveld, Manley, Abramson, Caffey, Cochran, Collier, Ebi, Engel, Farrington, Finucane, Hale, Halpern, Harville, Hart, Hswen, Kirkpatrick, McEwen, Morris, Orbach, Palinkas, Partyka, Porter, Prather, Rowles, Scott, Seeman, Solo-Gabriele, Svendsen, Tincher, Trtanj, Walker, Yehuda, Yip, Yoskowitz and Singer. The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

© Copyright © 2020 Sandifer, Knapp, Lichtveld, Manley, Abramson, Caffey, Cochran, Collier, Ebi, Engel, Farrington, Finucane, Hale, Halpern, Harville, Hart, Hswen, Kirkpatrick, McEwen, Morris, Orbach, Palinkas, Partyka, Porter, Prather, Rowles, Scott, Seeman, Solo-Gabriele, Svendsen, Tincher, Trtanj, Walker, Yehuda, Yip, Yoskowitz and Singer. The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop

Human Health and Socioeconomic Effects of the Deepwater Horizon Oil Spill in the Gulf of Mexico

The Deepwater Horizon (DWH) oil spill is the only declared Spill of National Significance in US history, and it significantly impacted the health of people and communities in the Gulf of Mexico region. These impacts amplified adverse effects of prior disasters and may compound those of future traumas. Studies, both to date and ongoing, show some negative mental and physical health outcomes associated with DWH in some spill workers, as well as some coastal residents in all Gulf States. The spill was also associated with negative effects in the living resources, tourism, and recreation sectors, at least in the short term. Compared with others, people dependent on these sectors reported more health and financial concerns. Consumer concerns about the safety and marketability of seafood persisted well after data demonstrated very low risk. Parents were concerned about possible exposures of children as they played on beaches, but this risk was found to be minor. Spill-related stress was an overarching factor associated with adverse health outcomes, and some residents reported greater stress from navigating the legal and claims processes following the spill than from the spill itself. Research revealed a serious lack of baseline health, environmental, and socioeconomic data against which to compare spill effects. This finding highlighted the need for ongoing observing systems to monitor health and socioeconomic parameters and establish continuous baselines of such information

Attentional bias modification treatment for depression: Study protocol for a randomized controlled trial

Theoretical models and empirical research point to negatively biased attention as a maintaining factor in depression. Although preliminary studies suggest experimentally modifying attentional biases (i.e., attentional bias modification; ABM) reduces depression symptoms and depression risk, relatively few rigorous studies with clinical samples have been completed. This clinical trial examines the impact of ABM on a sample of adults (N = 123) with elevated depression severity who also exhibit at least modest levels of negatively biased attention prior to treatment. Participants will be randomly assigned to either active ABM, placebo ABM, or an assessment-only control condition. Individuals assigned to ABM will complete 5 trainings per week (2 in-clinic, 3 brief trainings at-home) during a four-week period. Throughout this four-week period, participants will complete weekly assessments of symptom severity and putative treatment mediators measured across different levels of analysis (e.g., eye tracking, behavioral measures, and functional Magnetic Resonance Imaging). This article details the rationale and design of the clinical trial, including methodological issues that required more extensive consideration. Our findings may not only point to an easily-accessible, efficacious treatment for depression but may also provide a meaningful test of whether a theoretically important construct, negatively biased attention, maintains depression