Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study.

BACKGROUND AND PURPOSE: The habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND METHODS: Ten healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping. RESULTS: The 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all P < .05). Additionally, 3T MP2RAGE provided reliable habenula manual and MAGeTbrain segmentation volume estimates with greater reproducibility. T1-mapping derived from MP2RAGE was highly reliable, and susceptibility contrast was highly nonuniform within the habenula. CONCLUSIONS: We identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.

Response of ocean acidification to atmospheric carbon dioxide removal.

Artificial CO2 removal from the atmosphere (also referred to as negative CO2 emissions) has been proposed as a potential means to counteract anthropogenic climate change. Here we use an Earth system model to examine the response of ocean acidification to idealized atmospheric CO2 removal scenarios. In our simulations, atmospheric CO2 is assumed to increase at a rate of 1% per year to four times its pre-industrial value and then decreases to the pre-industrial level at a rate of 0.5%, 1%, 2% per year, respectively. Our results show that the annual mean state of surface ocean carbonate chemistry fields including hydrogen ion concentration ([H+]), pH and aragonite saturation state respond quickly to removal of atmospheric CO2. However, the change of seasonal cycle in carbonate chemistry lags behind the decline in atmospheric CO2. When CO2 returns to the pre-industrial level, over some parts of the ocean, relative to the pre-industrial state, the seasonal amplitude of carbonate chemistry fields is substantially larger. Simulation results also show that changes in deep ocean carbonate chemistry substantially lag behind atmospheric CO2 change. When CO2 returns to its pre-industrial value, the whole-ocean acidity measured by [H+] is 15%-18% larger than the pre-industrial level, depending on the rate of CO2 decrease. Our study demonstrates that even if atmospheric CO2 can be lowered in the future as a result of net negative CO2 emissions, the recovery of some aspects of ocean acidification would take decades to centuries, which would have important implications for the resilience of marine ecosystems.

Occurrence differences of hexachlorobutadiene and chlorobenzenes in road dust and roadside soil media in an industrial and residential mixed area in Eastern China.

The road dust and roadside soil can act as both sinks and sources of hexachlorobutadiene (HCBD) and chlorobenzenes (CBzs), but comparative research on these two adjacent media is extremely limited. In this study, HCBD and CBzs were simultaneously analyzed in road dust and roadside soil samples from an area containing both industrial factories and residential communities in Eastern China. The road dust there was found to have 2-6 times higher contents of HCBD (mean 1.14 ng/g, maximum 6.44 ng/g) and ∑Cl3-Cl6CBzs (22.8 ng/g, 90.6 ng/g) than those in the roadside soil. The spatial distributions of HCBD and CBzs in road dusts were affected by various types of sources, showing no significant discrepancy among the sites. On the contrast, HCBD and CBzs contamination in roadside soils occurring near several factories were strongly correlated to their industrial point sources. Risk assessments showed, at current contamination levels in the road dust and roadside soil, HCBD and CBzs are not likely to induce carcinogenic or non-carcinogenic risks to residents in the studied area. Nevertheless, road dust ingestion, as the major exposure pathway of HCBD and CBzs, should be avoided to reduce the exposure risk. These findings based on the contamination differences between two media provide a new perspective and evidence for screening important sources and exposure pathway of HCBD and CBzs, which would be helpful to their source identification and risk control.

Automatic Video Analysis Framework for Exposure Region Recognition in X-Ray Imaging Automation.

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer.

Characterization of Atmospheric Processes of Brevetoxins in Sea Spray Aerosols from Red Tide Events.

Atmospheric processes can affect the longevity of harmful toxins in sea spray aerosols (SSA). This study characterized the degradation of brevetoxin (BTx) in SSA under different environmental conditions. The samples of seawater collected during a Karenia brevis bloom in Manasota, Florida, were nebulized into a large outdoor photochemical chamber to mimic the atmospheric oxidation of aerosolized toxins and then aged in the presence or absence of sunlight and/or O3. Aerosol samples were collected during the aging process using a Particle-Into-Liquid Sampler. Their BTx concentrations were measured using an enzyme-linked immuno-sorbent assay (ELISA) and high-performance liquid chromatography/tandem mass spectroscopy. The BTx ozonolysis rate constant measured by ELISA was 5.74 ± 0.21 × 103 M-1 s-1. The corresponding lifetime for decay of 87.5% BTx in the presence of 20 ppb of O3 was 7.08 ± 0.26 h, suggesting that aerosolized BTx can still travel long distances at night before SSA deposition. BTx concentrations in SSA decreased more rapidly in the presence of sunlight than in its absence due to oxidation with photochemically produced OH radicals.

Prediction of Phase State of Secondary Organic Aerosol Internally Mixed with Aqueous Inorganic Salts.

In the presence of inorganic salts, secondary organic aerosol (SOA) undergoes liquid-liquid phase separation (LLPS), liquid-solid phase separation, or a homogeneous phase in ambient air. In this study, a regression model was derived to predict aerosol phase separation relative humidity (SRH) for various organic and inorganic mixes. The model implemented organic physicochemical parameters (i.e., oxygen to carbon ratio, molecular weight, and hydrogen-bonding ability) and the parameters related to inorganic compositions (i.e., ammonium, sulfate, nitrate, and water). The aerosol phase data were observed using an optical microscope and also collected from the literature. The crystallization of aerosols at the effloresce RH (ERH) was semiempirically predicted with a neural network model. Overall, the greater SRH appeared for the organic compounds with the lower oxygen to carbon ratios or the greater molecular weight and the higher aerosol acidity or the larger fraction of inorganic nitrate led to the lower SRH. The resulting model has been demonstrated for three different chamber-generated SOA (originated from ß-pinene, toluene, and 1,3,5-trimethylbenzene), which were internally mixed with the inorganic aqueous system of ammonium-sulfate-water. For all three SOA systems, both observations and model predictions showed LLPS at RH <80%. In the urban atmosphere, LLPS is likely a frequent occurrence for the typical anthropogenic SOA, which originates from aromatic and alkane hydrocarbon.

An automated ASPECTS method with atlas-based segmentation.

BACKGROUND AND PURPOSE: As a simple and reliable systematic method to evaluate the early ischemic changes in the blood supply region of the middle cerebral artery of patients with ischemic stroke, the Alberta Stroke Program Early CT score (ASPECTS) can be used for rapid semi-quantitative evaluation of ischemic lesions, which is helpful to select potential candidates for intravenous and intra-arterial therapies, determine the thrombolytic effect and long-term prognosis. This method mainly relies on doctors' visual observation. However, due to different levels of doctor's experience, the poor inter-reader agreement may result in errors in the final ASPECTS. The purpose of this work was to propose an automated semi-quantitative method for the diagnosis of acute ischemic stroke based on non-contrast computed tomography (NCCT), to provide a reference for doctors in the diagnosis and evaluation. METHODS: NCCT data from a total of 90 patients were included for auto-ASPECTS training and testing. After preprocessing CT images, the regions of interest (ROI) for ASPECTS were labeled using atlas-based segmentation. The mean difference, mean ratio and brain density shifts (BDS) of the corresponding regions of the contralateral brain were used as the standard for quantitative analysis. The auto-ASPECTS method was developed and validated to predict early ischemic changes whose performance was evaluated by the agreement (accuracy) of predictions and consensus scores of two observers. RESULTS: A comparison was made among the results on mean difference, mean ratio, BDS and the combination of multiple parameters as the standard. The result of using BDS alone was relatively better than the result of using any other parameter alone or any combination of multiple parameters, and accuracy in the test set was 0.80. In the test set, accuracy with using different BDS thresholds increased by 6.67% compared with using the consistent BDS threshold. After dichotomy of auto-ASPECTS and consensus scores with the threshold of 7, the agreement of them was 83.3% and there was no significant difference between the two distributions (p = 0.344) in McNemar test. CONCLUSIONS: The proposed auto-ASPECTS method for NCCT images can provide useful information for early diagnosis and evaluation of patients with acute ischemic stroke (AIS).

Simulation-guided engineering of an enzyme-powered three dimensional DNA nanomachine for discriminating single nucleotide variants.

Single nucleotide variants (SNVs) are important both clinically and biologically because of their profound biological consequences. Herein, we engineered a nicking endonuclease-powered three dimensional (3D) DNA nanomachine for discriminating SNVs with high sensitivity and specificity. Particularly, we performed a simulation-guided tuning of sequence designs to achieve the optimal trade-off between device efficiency and specificity. We also introduced an auxiliary probe, a molecular fuel capable of tuning the device in solution via noncovalent catalysis. Collectively, our device produced discrimination factors comparable with commonly used molecular probes but improved the assay sensitivity by ∼100 times. Our results also demonstrate that rationally designed DNA probes through computer simulation can be used to quantitatively improve the design and operation of complexed molecular devices and sensors.

Heterogeneous Photo-oxidation of SO2 in the Presence of Two Different Mineral Dust Particles: Gobi and Arizona Dust.

The impact of authentic mineral dust particles sourced from the Gobi Desert (GDD) on the kinetic uptake coefficient of SO2 was studied under varying environments (humidity, O3, and NOx) using both an indoor chamber and an outdoor chamber. There was a significant increase in the kinetic uptake coefficient of SO2 (γSO42-,light) for GDD particles under UV light compared to the value (γSO42-,dark) under dark conditions at various relative humidities (RH) ranging from 20% to 80%. In both the presence and the absence of O3 and NOx, γSO42-,light and γSO42-,dark greatly increased with increasing RH. The resulting γSO42-,light of GDD particles was also compared to that of Arizona Test Dust (ATD) particles. The γSO42-,light values of GDD were 2 to 2.5 times greater than those of ATD for all RH levels. To understand the photocatalytic act of dust particles, both GDD and ATD were characterized for the metal element composition of fresh particles, the aerosol acidity of aged particles, and the hygroscopic properties of both fresh and aged particles. We conclude that the difference in the formation of sulfate between GDD and ATD particles is regulated mainly by the quantity of the semiconductive metals in dust particles and partially by hygroscopic properties.

Prediction of delivery of organic aerosols onto air-liquid interface cells in vitro using an electrostatic precipitator.

To better characterize biological responses to atmospheric organic aerosols, the efficient delivery of aerosol to in vitro lung cells is necessary. In this study, chamber generated secondary organic aerosol (SOA) entered the commercialized exposure chamber (CULTEX® Radial Flow System Compact) where it interfaced with an electrostatic precipitator (ESP) (CULTEX® Electrical Deposition Device) and then deposited on a particle collection plate. This plate contained human lung cells (BEAS-2B) that were cultured on a membrane insert to produce an air-liquid interface (ALI). To augment in vitro assessment using the ESP exposure device, the particle dose was predicted for various sampling parameters such as particle size, ESP deposition voltage, and sampling flowrate. The dose model was evaluated against the experimental measured mass of collected airborne particles. The high flowrate used in this study increased aerosol dose but failed to achieve cell stability. For example, RNA in the ALI BEAS-2B cells in vitro was stable at 0.15L/minute but decayed at high flowrates. The ESP device and the resulting model were applied to in vitro studies (i.e., viability and IL-8 expression) of toluene SOA using ALI BEAS-2B cells with a flowrate of 0.15L/minute, and no cellular RNA decay occurred.