Increasing Atmospheric Humidity and CO\u3csub\u3e2\u3c/sub\u3e Concentration Alleviate Forest Mortality Risk

Climate-induced forest mortality is being increasingly observed throughout the globe. Alarmingly, it is expected to exacerbate under climate change due to shifting precipitation patterns and rising air temperature. However, the impact of concomitant changes in atmospheric humidity and CO2 concentration through their influence on stomatal kinetics remains a subject of debate and inquiry. By using a dynamic soil–plant–atmosphere model, mortality risks associated with hydraulic failure and stomatal closure for 13 temperate and tropical forest biomes across the globe are analyzed. The mortality risk is evaluated in response to both individual and combined changes in precipitation amounts and their seasonal distribution, mean air temperature, specific humidity, and atmospheric CO2 concentration. Model results show that the risk is predicted to significantly increase due to changes in precipitation and air temperature regime for the period 2050–2069. However, this increase may largely get alleviated by concurrent increases in atmospheric specific humidity and CO2 concentration. The increase in mortality risk is expected to be higher for needleleaf forests than for broadleaf forests, as a result of disparity in hydraulic traits. These findings will facilitate decisions about intervention and management of different forest types under changing climate

Restraint use and risky driving behaviors across drug types and drug and alcohol combinations for drivers involved in a fatal motor vehicle collision on U.S. roadways

Background While driving impaired is a well-recognized risk factor for motor vehicle (MV) crash, recent trends in recreational drug use and abuse may pose increased threats to occupant safety. This study examines mechanisms through which drug and/or alcohol combinations contribute to fatal MV crash. Methods The Fatality Analysis Reporting System (FARS) for 2008–2013 was used to examine drugs, alcohol, driver restraint use, driver violations/errors and other behaviors of drivers of passenger vehicles who were tested for both alcohol and drugs (n = 79,932). Statistical analysis was based on Chi-square tests and multivariable logistic regression. Associations of restraint use and other outcomes with alcohol and drug use were measured by estimated odds ratios (ORs) and 95 % confidence intervals (95 % CIs). Results More than half (54.8 %) of the study population were positive for drugs or alcohol at the time of crash. Approximately half of drivers were belted, but this varied from 67.1 % (unimpaired) to 33.0 % (drugs plus alcohol). Compared to the unimpaired, the odds of a driver being unbelted varied: alcohol and cannabis (OR 3.70, 95 % CI 3.44–3.97), alcohol only (3.50,3.36–3.65), stimulants (2.13,1.91–2.38), depressants (2.09,1.89–2.31), narcotics (1.84,1.67–2.02) and cannabis only (1.55,1.43–1.67). Compared to belted drivers, unbelted drivers were over 4 times more likely to die. Driving violations varied across drug/drug alcohol combinations. Speed-related violations were higher for drivers positive for stimulants, alcohol, cannabis, and cannabis plus alcohol, with a more than two fold increase for alcohol and cannabis (2.36, 2.05, 2.71). Conclusions Mechanisms through which drugs, alcohol and substance combinations produce increased risks to occupant safety include lowered restraint use and increases in risky driving behaviors, including speeding, lane, passing, turning and signal/sign violations

Restraint use and risky driving behaviors across drug types and drug and alcohol combinations for drivers involved in a fatal motor vehicle collision on U.S. roadways

Background While driving impaired is a well-recognized risk factor for motor vehicle (MV) crash, recent trends in recreational drug use and abuse may pose increased threats to occupant safety. This study examines mechanisms through which drug and/or alcohol combinations contribute to fatal MV crash. Methods The Fatality Analysis Reporting System (FARS) for 2008–2013 was used to examine drugs, alcohol, driver restraint use, driver violations/errors and other behaviors of drivers of passenger vehicles who were tested for both alcohol and drugs (n = 79,932). Statistical analysis was based on Chi-square tests and multivariable logistic regression. Associations of restraint use and other outcomes with alcohol and drug use were measured by estimated odds ratios (ORs) and 95 % confidence intervals (95 % CIs). Results More than half (54.8 %) of the study population were positive for drugs or alcohol at the time of crash. Approximately half of drivers were belted, but this varied from 67.1 % (unimpaired) to 33.0 % (drugs plus alcohol). Compared to the unimpaired, the odds of a driver being unbelted varied: alcohol and cannabis (OR 3.70, 95 % CI 3.44–3.97), alcohol only (3.50,3.36–3.65), stimulants (2.13,1.91–2.38), depressants (2.09,1.89–2.31), narcotics (1.84,1.67–2.02) and cannabis only (1.55,1.43–1.67). Compared to belted drivers, unbelted drivers were over 4 times more likely to die. Driving violations varied across drug/drug alcohol combinations. Speed-related violations were higher for drivers positive for stimulants, alcohol, cannabis, and cannabis plus alcohol, with a more than two fold increase for alcohol and cannabis (2.36, 2.05, 2.71). Conclusions Mechanisms through which drugs, alcohol and substance combinations produce increased risks to occupant safety include lowered restraint use and increases in risky driving behaviors, including speeding, lane, passing, turning and signal/sign violations

Imaging molecular orbitals with laser-induced electron tunneling spectroscopy

Photoelectron spectroscopy in intense laser fields has proven to be a powerful tool for providing detailed insights into molecular structure. The ionizing molecular orbital, however, has not been reconstructed from the photoelectron spectra, mainly due to the fact that its phase information can be hardly extracted. In this work, we propose a method to retrieve the phase information of the ionizing molecular orbital with laser-induced electron tunneling spectroscopy. By analyzing the interference pattern in the photoelectron spectrum, the weighted coefficients and the relative phases of the constituent atomic orbitals for a molecular orbital can be extracted. With this information we reconstruct the highest occupied molecular orbital of N$_2$. Our work provides a reliable and general approach for imaging of molecular orbitals with the photoelectron spectroscopy.Comment: 6 pages, 4 figures, including Supplementary Material

Laser-sub-cycle two-dimensional electron momentum mapping using orthogonal two-color fields

The two-dimensional sub-cycle-time to electron momentum mapping provided by orthogonal two-color laser fields is applied to photoelectron spectroscopy. Using neon as the example we gain experimental access to the dynamics of emitted electron wave packets in electron momenta spectra measured by coincidence momentum imaging. We demonstrate the opportunities provided by this time-to-momentum mapping by investigating the influence of the parent ion on the emitted electrons on laser-sub-cycle times. It is found that depending on their sub-cycle birth time the trajectories of photoelectrons are affected differently by the ion's Coulomb field

Protective Role of Rho Guanosine Diphosphate Dissociation Inhibitor, Ly-GDI, in Pulmonary Alveolitis

Growing evidences indicate that Ly-GDI, an inhibitory protein of Rho GTPases, plays an essential role in regulating actin cytoskeletal alteration which is indispensible for the process such as phagocytosis. However, the role of Ly-GDI in inflammation remains largely unknown. In the current study, we found that Ly-GDI expression was significantly decreased in the IgG immune complex-injured lungs. To determine if Ly-GDI might regulate the lung inflammatory response, we constructed adenovirus vectors that could mediate ectopic expression of Ly-GDI (Adeno-Ly-GDI). In vivo mouse lung expression of Ly-GDI resulted in a significant attenuation of IgG immune complex-induced lung injury, which was due to the decreased pulmonary permeability and lung inflammatory cells, especially neutrophil accumulation. Upon IgG immune complex deposition, mice with Ly-GDI over-expression in the lungs produced significant less inflammatory mediators (TNF-α, IL-6, MCP-1, and MIP-1α) in bronchoalveolar lavage fluid when compared control mice receiving airway injection of Adeno-GFP. Mechanically, IgG immune complex-induced NF-κB activity was markedly suppressed by Ly-GDI in both alveolar macrophages and lungs as measured by luciferase assay and electrophoretic mobility shift assay. These findings suggest that Ly-GDI is a critical regulator of inflammatory injury after deposition of IgG immune complexes and that it negatively regulates the lung NF-κB activity

Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network

© 1980-2012 IEEE. Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE) 0.971]

Factors affecting HPV infection in U.S. and Beijing females: A modeling study

BackgroundHuman papillomavirus (HPV) infection is an important carcinogenic infection highly prevalent among many populations. However, independent influencing factors and predictive models for HPV infection in both U.S. and Beijing females are rarely confirmed. In this study, our first objective was to explore the overlapping HPV infection-related factors in U.S. and Beijing females. Secondly, we aimed to develop an R package for identifying the top-performing prediction models and build the predictive models for HPV infection using this R package.MethodsThis cross-sectional study used data from the 2009–2016 NHANES (a national population-based study) and the 2019 data on Beijing female union workers from various industries. Prevalence, potential influencing factors, and predictive models for HPV infection in both cohorts were explored.ResultsThere were 2,259 (NHANES cohort, age: 20–59 years) and 1,593 (Beijing female cohort, age: 20–70 years) participants included in analyses. The HPV infection rate of U.S. NHANES and Beijing females were, respectively 45.73 and 8.22%. The number of male sex partners, marital status, and history of HPV infection were the predominant factors that influenced HPV infection in both NHANES and Beijing female cohorts. However, condom application was not an independent influencing factor for HPV infection in both cohorts. R package Modelbest was established. The nomogram developed based on Modelbest package showed better performance than the nomogram which only included significant factors in multivariate regression analysis.ConclusionCollectively, despite the widespread availability of HPV vaccines, HPV infection is still prevalent. Compared with condom promotion, avoidance of multiple sexual partners seems to be more effective for preventing HPV infection. Nomograms developed based on Modelbest can provide improved personalized risk assessment for HPV infection. Our R package Modelbest has potential to be a powerful tool for future predictive model studies

Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance

Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil-plant-atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics