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Background/Objectives: Cardiovascular diseases are the primary cause of global morbidity and mortality, with cardiovascular health (CVH) remaining well below the ideal level and showing minimal improvement in the U.S. population over recent years. Bisphenol A (BPA), a pervasive environmental contaminant, has emerged as a potential contributor to adverse cardiovascular outcomes. This cross-sectional study delves into the impact of BPA exposure on achieving optimal CVH, as assessed by the Life's Essential 8 metric, among U.S. adults. Methods: Analyzing data from 6635 participants in the National Health and Nutrition Examination Survey (NHANES) collected between 2005 and 2016, BPA exposure was quantified through urinary BPA levels, while optimal CVH was defined using the American Heart Association's Life's Essential 8 criteria, scoring between 80 and 100. Multivariable logistic regression and propensity score matching were employed to evaluate the association between BPA exposure and CVH. Results: This study reveals that individuals in the highest tertile of urinary BPA levels were 27% less likely to attain optimal CVH compared with those in the lowest tertile (OR, 0.73; 95% CI: 0.59–0.92). This negative association persisted across diverse demographics, including age, sex, and race, mirrored in the link between urinary BPA levels and health factor scores. Conclusions: The findings underscore the potential benefits of reducing BPA exposure in enhancing the prevalence of optimal CVH and mitigating the burden of cardiovascular disease. Given the widespread use of BPA, ongoing monitoring of BPA's impact on CVH is essential. Further studies are necessary to elucidate the long-term and causative connections between BPA and CVH. These insights contribute to understanding the complex interplay between environmental factors and CVH outcomes, informing targeted interventions to mitigate cardiovascular disease risk within the population. [ABSTRACT FROM AUTHOR]

Background: The process of the globally aging population has been accelerating, leading to an increasing social burden. As people age, the musculoskeletal system will gradually go through a series of degenerative and loss of function and eventually develop age-related musculoskeletal diseases, like sarcopenia, osteoporosis, and osteoarthritis. On the other hand, several studies have shown that polyunsaturated fatty acids (PUFAs) possess various important physiological functions on the health of muscles, bones, and joints. Objective: This narrative review paper provides a summary of the literature about the effects and mechanisms of PUFAs on age-related musculoskeletal diseases for the prevention and management of these diseases. Methods: Web of Science, PubMed, Science Direct, and Scopus databases have been searched to select the relevant literature on epidemiological, cellular, and animal experiments and clinical evidence in recent decades with keywords "polyunsaturated fatty acids", "PUFAs", "omega-3", "omega-6", "musculoskeletal diseases", "sarcopenia", "osteoporosis", "osteoarthritis", and so on. Results: PUFAs could prevent and treat age-related musculoskeletal diseases (sarcopenia, osteoporosis, and osteoarthritis) by reducing oxidative stress and inflammation and controlling the growth, differentiation, apoptosis, and autophagy of cells. This review paper provides comprehensive evidence of PUFAs on age-related musculoskeletal diseases, which will be helpful for exploitation into functional foods and drugs for their prevention and treatment. Conclusions: PUFAs could play an important role in the prevention and treatment of sarcopenia, osteoporosis, and osteoarthritis. [ABSTRACT FROM AUTHOR]

By utilizing progress in millijoule-level pulsed fiber lasers operating in the 1.96 µm spectral range, we introduce a concept utilizing a spaceborne differential absorption barometric lidar designed to operate within the 1.96 µm CO2 absorption band for remote sensing of Martian atmospheric properties. Our focus is on the online wavelength situated in the trough region of two absorption lines, selected due to its insensitivity to laser frequency variations, thus mitigating the necessity for stringent laser frequency stability. Our investigation revolves around a compact lidar configuration, featuring reduced telescope dimensions and lower laser pulse energies. These adjustments are geared towards minimizing costs for potential forthcoming Mars missions. The core measurement objectives encompass the determination of column CO2 absorption optical depth, columnar CO2 abundance, surface atmospheric pressure, and vertical distributions of dust and cloud layers. Through the amalgamation of surface pressure data with atmospheric temperature insights garnered from sounders and utilizing the barometric formula, the prospect of deducing atmospheric pressure profiles becomes feasible. Simulation studies validate the viability of our approach. Notably, the precision of Martian surface pressure measurements is projected to surpass 1 Pa when the aerial dust optical depth is projected to be under 0.7, a typical airborne dust scenario on Mars, considering a horizontal averaging span of 10 km. [ABSTRACT FROM AUTHOR]

Nighttime PM2.5 detection by remote sensing can expand understanding of PM2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs. [ABSTRACT FROM AUTHOR]

We present a method to determine the geometric form factor of a ground-based lidar using simultaneous lidar measurements made from the ground and from space. The theoretical basis is described. The feasibility of the method is demonstrated by applying it to the measurement data acquired by the Cloud Aerosol Lidar Infrared Pathfinder Satellite Observation (CALIPSO) lidar and a ground-based lidar located at the Hampton University (37.02[degrees]N, 76.34[degrees]W). The geometric factors with different aerosol conditions are retrieved. OCIS codes: 010.3640, 290.5860, 280.0280, 010.1280.

We present observations of cirrus clouds from June 2006 to July 2007 performed by using a two-wavelength lidar located at Hampton University. For this time period, cirrus clouds were observed mostly in 7-13.5 km altitudes. Data analyses have been performed focusing on a color-ratio retrieval. In total, 86,369 samples from 1,689 profiles (1 min average and 15 m range resolution) containing cirrus clouds with attenuated backscatter ratio (ratio of attenuated total backscatter to the molecular backscatter) larger than 10 have been selected. The cirrus color ratio distribution shows a peak value at about 0.88 and a full width at half-maximum of 0.12. OCIS codes: 280.3640, 010.1615.

Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.

Backscatter and extinction from water clouds are well-understood, both theoretically and experimentally, and thus changes to the expected measurement of layer-integrated attenuated backscatter can be used to infer the optical properties of overlying layers. In this paper we offer a first look at a new retrieval technique that uses CALIPSO measurements of opaque water clouds to derive optical depths and Ångström exponents for overlying aerosol layers.

In a previous study we evaluated the above-cloud aerosol optical depth (AOD) retrieval at 532 nm in the CALIOP version 3 (V3) data products for two selected spatial domains along the Saharan dust transport pathway and African smoke transport pathway. In that study we rescaled the V3 level-1 (L1) data to compensate for known V3 calibration biases, and then derived aerosol intrinsic properties such as lidar ratio (Sa) and particulate depolarization ratio (PDR) for comparison with the CALIOP dust and smoke aerosol models. The calibration of the recently released version 4 (V4) CALIPSO L1 data product is significantly improved over V3. So in this paper we repeat our previous analysis using the new V4 L1 data. A comparison shows that our rescaled V3 and the new V4 data are different only by ~1% in the two selected spatial domains. The retrieved AOD values decrease by ~2.6% in both domains from V3 to V4. When the data is screened to exclude weakly scattering layers, the median Sa retrieved from the V4 L1 data in the dust transport region is reduced by 1.4 sr to 43.0±8.3 sr. The median Sa value in the smoke transport region is increased by 0.8 sr to 71.2±15.1 sr. The PDR values remain almost unchanged for the screened data.

We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photoncounting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment. OCIS codes: 280.3640, 040.5160, 270.5290.

Extinction-to-backscatter ratio or lidar ratio is a key parameter in the issue of backscatter-lidar inversions. The lidar ratio of Asian dust was observed with a high-spectral-resolution lidar and a combined Raman elastic-backscatter lidar during the springs of 1998 and 1999. The measured values range from 42 to 55 sr in most cases, with a mean of 51 sr. These values are significantly larger than those predicted by the Mie computations that incorporate measured Asian dust size distributions and a range of refractive index with a typical value of 1.55-0.005i. The enhancement of lidar ratio is mostly due to the nonsphericity of dust particles, as indicated by the T-matrix calculations for spheroid particles and a number of other theoretical studies. In addition, possible contamination of urban aerosols may also contribute somewhat in optically thin cases. Mie theory, although it can well describe spherical particle scattering, will not be sufficient to represent the scattering characteristics of irregular particles such as Asian dust, especially in directions larger than approximately 90 [degrees], when the size parameter is large. OCIS codes: 010.3640, 010.1110, 010.1310, 290.1310, 290.5850, 280.3640.

Output signal electrons from photomultiplier tubes (PMTs) have neither a Gaussian nor a Poisson distribution because of changes induced by multiplication when the number of input signal photons and dark electrons is fewer than ~100. Therefore the assumption of a Gaussian distribution of signal electrons cannot be used in simulations for space lidar observations with PMTs, for which the number of return signal photons is normally small. A theory is introduced for analog detection with PMTs that have Poisson-distributed secondary-electron emission at each dynode stage. The theory is validated by straightforward numerical simulations. It is shown that the multiplication in PMTs is a multiply stochastic Poisson process and that the distribution of output signal electrons can be interpreted basically as Neyman type A. Analysis by the threshold method of cloud detection with a space lidar shows considerable difference between a Gaussian approximation and the exact distribution. The result indicates that the threshold level must be optimized for the exact distribution. Return signals were simulated for a proposed space lidar, and cloud detection with the threshold method was demonstrated. OCIS codes: 040.5250, 280.3640.

Spaceborne lidar observations have a great potential to improve our knowledge of the atmosphere. However, contrary to ground-based or airborne lidar measurements in observations from space multiple scattering (MS) has to be accounted for under all atmospheric conditions. A simple way to achieve this is to introduce the approximative MS factor F in the single scattering lidar equation. We determined F for measurements of aerosols, ice and water clouds as they were planned with the Japanese space lidar ELISE. We found that F is almost constant in cirrus clouds and in water clouds which are penetrable by lidar. In aerosol layers F depends on penetration depth, extinction coefficient and aerosol type. In comparison to LITE the MS factor is smaller (for aerosols and water clouds) or almost equal (cirrus). Index Terms--Multiple scattering, space lidar.

The features detected in monolayer atmospheric columns sounded by the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) onboard the same satellite. Using the IIR's three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are confirmed thanks to the IIR observations in the tropics and in the midlatitudes respectively. IIR observations are of relatively little help in deriving high confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those from clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke respectively are found to be misclassified cloud layers by the IIR measurements. [ABSTRACT FROM AUTHOR]