In this study, we investigated lipopolysaccharide (LPS)-induced cognitive impairment and neuroinflammation in C57BL/6J mice by using behavioral tests, immunofluorescence, enzyme-linked immunosorbent assay (ELISA) and Western blot. We found that LPS treatment leads to sickness behavior and cognitive impairment in mice as shown in the Morris water maze and passive avoidance test, and these effects were accompanied by microglia activation (labeled by ionized calcium binding adaptor molecule-1, IBA-1) and neuronal cell loss (labeled by microtubule-associated protein 2, MAP-2) in the hippocampus. The levels of interleukin-4 (IL-4) and interleukin-10 (IL-10) in the serum and brain homogenates were reduced by the LPS treatment, while the levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), prostaglandin E2 (PGE 2 ) and nitric oxide (NO) were increased. In addition, LPS promoted the expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) in the brain homogenates. The Western blot analysis showed that the nuclear factor kappa B (NF-κB) signaling pathway was activated in the LPS groups. Furthermore, VIPER, which is a TLR-4-specific inhibitory peptide, prevented the LPS-induced neuroinflammation and cognitive impairment. These data suggest that LPS induced cognitive impairment and neuroinflammation via microglia activation by activating the NF-kB signaling pathway; furthermore, we compared the time points, doses, methods and outcomes of LPS administration between intraperitoneal and intracerebroventricular injections of LPS in LPS-induced neuroinflammation and cognitive impairment, and these data may provide additional insight for researchers performing neuroinflammation research.

Published estimates of methane emissions from atmospheric data (top-down approaches) exceed those from source-based inventories (bottom-up approaches), leading to conflicting claims about the climate implications of fuel switching from coal or petroleum to natural gas. Based on data from a coordinated campaign in the Barnett Shale oil and gas-producing region of Texas, we find that top-down and bottom-up estimates of both total and fossil methane emissions agree within statistical confidence intervals (relative differences are 10% for fossil methane and 0.1% for total methane). We reduced uncertainty in top-down estimates by using repeated mass balance measurements, as well as ethane as a fingerprint for source attribution. Similarly, our bottom-up estimate incorporates a more complete count of facilities than past inventories, which omitted a significant number of major sources, and more effectively accounts for the influence of large emission sources using a statistical estimator that integrates observations from multiple ground-based measurement datasets. Two percent of oil and gas facilities in the Barnett accounts for half of methane emissions at any given time, and high-emitting facilities appear to be spatiotemporally variable. Measured oil and gas methane emissions are 90% larger than estimates based on the US Environmental Protection Agency's Greenhouse Gas Inventory and correspond to 1.5% of natural gas production. This rate of methane loss increases the 20-y climate impacts of natural gas consumed in the region by roughly 50%. methane emissions | oil and gas emissions | greenhouse gas footprint | natural gas supply chain | Barnett Shale M ethane (CH 4 ), the principal component of natural gas, is a powerful greenhouse gas. Although natural gas emits less carbon dioxide (CO 2 ) per unit of energy than coal or oil when burned, CH 4 losses during the production, processing, transportation, and use of natural gas reduce its climate advantage compared with other fossil fuels. For example, if CH 4 losses are large enough (e.g., ∼3% of production), new natural gas power plants can cause greater climate damage than new coal plants for decades or longer (∼1% when comparing natural gas to diesel freight trucks) (1).The lack of current data on CH 4 emissions, magnified by intense public concern over the broader environmental implications of shale gas development, has stimulated significant research to improve estimates of CH 4 emissions (2-18). A recurring theme in recent literature is that "top-down" (TD) approaches produce estimates that are significantly higher than those from "bottomup" (BU) approaches. Concerns about available inventories and divergent TD and BU estimates create confusion regarding policy formulation and leave room for conflicting claims about the greenhouse gas implications of increased use of natural gas.TD approaches for estimating total CH 4 emissions at the regional or larger scale include airborne mass balance (2-4, 19), atmospheric transport models (5,6,(20)(21)(22)(23), and enhancem...

Current methods can illuminate the genome-wide activity of CRISPR-Cas9 nucleases, but are not easily scalable to the throughput needed to fully understand the principles that govern Cas9 specificity. Here we describe ‘circularization for high-throughput analysis of nuclease genome-wide effects by sequencing’ (CHANGE-seq), a scalable, automatable tagmentation-based method for measuring the genome-wide activity of Cas9 in vitro . We applied CHANGE-seq to 110 sgRNA targets across 13 therapeutically relevant loci in human primary T-cells and identified 201,934 off-target sites, enabling the training of a machine learning model to predict off-target activity. Comparing matched genome-wide off-target, chromatin modification and accessibility, and transcriptional data, we found that cellular off-target activity was two to four times more likely to occur near active promoters, enhancers, and transcribed regions. Finally, CHANGE-seq analysis of 6 targets across 8 individual genomes revealed that human single-nucleotide variation had significant effects on activity at ~15.2% of off-target sites analyzed. CHANGE-seq is a simplified, sensitive, and scalable approach to understanding the specificity of genome editors.

Methane emissions from the oil and gas industry (O&G) and other sources in the Barnett Shale region were estimated by constructing a spatially resolved emission inventory. Eighteen source categories were estimated using multiple data sets, including new empirical measurements at regional O&G sites and a national study of gathering and processing facilities. Spatially referenced activity data were compiled from federal and state databases and combined with O&G facility emission factors calculated using Monte Carlo simulations that account for high emission sites representing the very upper portion, or fat-tail, in the observed emissions distributions. Total methane emissions in the 25-county Barnett Shale region in October 2013 were estimated to be 72,300 (63,400-82,400) kg CH4 h(-1). O&G emissions were estimated to be 46,200 (40,000-54,100) kg CH4 h(-1) with 19% of emissions from fat-tail sites representing less than 2% of sites. Our estimate of O&G emissions in the Barnett Shale region was higher than alternative inventories based on the United States Environmental Protection Agency (EPA) Greenhouse Gas Inventory, EPA Greenhouse Gas Reporting Program, and Emissions Database for Global Atmospheric Research by factors of 1.5, 2.7, and 4.3, respectively. Gathering compressor stations, which accounted for 40% of O&G emissions in our inventory, had the largest difference from emission estimates based on EPA data sources. Our inventory's higher O&G emission estimate was due primarily to its more comprehensive activity factors and inclusion of emissions from fat-tail sites.