IKKα inactivation promotes Kras-initiated lung adenocarcinoma development through disrupting major redox regulatory pathways.

Lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are two distinct and predominant types of human lung cancer. IκB kinase α (IKKα) has been shown to suppress lung SCC development, but its role in ADC is unknown. We found inactivating mutations and homologous or hemizygous deletions in the CHUK locus, which encodes IKKα, in human lung ADCs. The CHUK deletions significantly reduced the survival time of patients with lung ADCs harboring KRAS mutations. In mice, lung-specific Ikkα ablation (IkkαΔLu ) induces spontaneous ADCs and promotes KrasG12D-initiated ADC development, accompanied by increased cell proliferation, decreased cell senescence, and reactive oxygen species (ROS) accumulation. IKKα deletion up-regulates NOX2 and down-regulates NRF2, leading to ROS accumulation and blockade of cell senescence induction, which together accelerate ADC development. Pharmacologic inhibition of NADPH oxidase or ROS impairs KrasG12D-mediated ADC development in IkkαΔLu mice. Therefore, IKKα modulates lung ADC development by controlling redox regulatory pathways. This study demonstrates that IKKα functions as a suppressor of lung ADC in human and mice through a unique mechanism that regulates tumor cell-associated ROS metabolism

Urban energy consumption and CO2 emissions in Beijing: current and future

This paper calculates the energy consumption and CO2 emissions of Beijing over 2005–2011 in light of the Beijing’s energy balance table and the carbon emission coefficients of IPCC. Furthermore, based on a series of energy conservation planning program issued in Beijing, the Long-range Energy Alternatives Planning System (LEAP)-BJ model is developed to study the energy consumption and CO2 emissions of Beijing’s six end-use sectors and the energy conversion sector over 2012–2030 under the BAU scenario and POL scenario. Some results are found in this research: (1) During 2005–2011, the energy consumption kept increasing, while the total CO2 emissions fluctuated obviously in 2008 and 2011. The energy structure and the industrial structure have been optimized to a certain extent. (2) If the policies are completely implemented, the POL scenario is projected to save 21.36 and 35.37 % of the total energy consumption and CO2 emissions than the BAU scenario during 2012 and 2030. (3) The POL scenario presents a more optimized energy structure compared with the BAU scenario, with the decrease of coal consumption and the increase of natural gas consumption. (4) The commerce and service sector and the energy conversion sector will become the largest contributor to energy consumption and CO2 emissions, respectively. The transport sector and the industrial sector are the two most potential sectors in energy savings and carbon reduction. In terms of subscenarios, the energy conservation in transport (TEC) is the most effective one. (5) The macroparameters, such as the GDP growth rate and the industrial structure, have great influence on the urban energy consumption and carbon emissions

Effects and mechanisms of a new multivitamin on chronic metabolic syndromes and aging

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Bacterial etiology in early re-admission patients with acute exacerbation of chronic obstructive pulmonary disease

Background: Repeatedly hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are often exposed to more antibiotics, but the distribution of pathogenic bacteria in these patients is poorly understood. The objectives of this study were to analyze the distribution of pathogenic bacteria and the risk factors associated with multidrug-resistant (MDR) bacteria infection in early re-admission patients with AECOPD.Methods: We retrospectively reviewed charts for patients with AECOPD admitted to our hospital between January 2011 and november 2012. The early re-admission group and non-early readmission group were determined by whether patients were readmitted within 31 days after discharge. Detection of potentially pathogenic microorganisms (PPMs) and MDR bacteria were analyzed. Logistic regression analysis was performed to identify independent risk factors for MDR bacteria infection.Results: PPMs were isolated from 230 (32.0%) cases of respiratory tract specimens; MDR bacteria accounted for 24.7% (57/230). Pseudomonas aeruginosa (43.7%), Klebsiella pneumoniae (15.6%), and Acinetobacter baumannii (12.5%) were the top three PPMs in the early readmission group, while the top three PPMs in the non-early readmission group were K. pneumoniae (23.7%), P. aeruginosa (21.2%), and Streptococcus pneumoniae (17.1%). Multivariate analysis showed that use of antibiotics within 2 weeks (odds ratio [OR] 8.259, 95% confidence interval [CI] 3.056-22.322, p = 0.000) was the independent risk factor for MDR bacteria infection.Conclusion: Non-fermentative Gram-negative bacilli (NFGNB) and enterobacteria were the predominant bacteria in early readmission patients with AECOPD. The detection rate of MDR bacteria was high which was related to the use of antibiotics within 2 weeks before admission in these patients.Keywords: AECOPD, re-admission, bacteria, multidrug-resistant (MDR), risk factors

Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion

The force field describing the calculated interaction between atoms or molecules is the key to the accuracy of many molecular dynamics (MD) simulation results. Compared with traditional or semi-empirical force fields, machine learning force fields have the advantages of faster speed and higher precision. We have employed the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations for machine learning, and successfully obtained the force field of the binary Fe-Co alloy. Molecular dynamics simulations of Fe-Co alloy carried out using this ACE force field predicted the correct phase transition range of Fe-Co alloy.Comment: 17 pages, 6 figure

U(1)xSU(2) Chern--Simons gauge theory of underdoped cuprate superconductors

The Chern-Simons bosonization with U(1)xSU(2) gauge field is applied to 2-D t-J model in the limit t >> J, to study the normal state properties of underdoped cuprate superconductors. We prove the existence of an upper bound on the partition function for holons in a spinon background, and we find the optimal spinon configuration saturating the upper bound on average--a coexisting flux phase and s+id-like RVB state. After neglecting the feedback of holon fluctuations on the U(1) field B and spinon fluctuations on the SU(2) field V, the holon field is a fermion and the spinon field is a hard--core boson. We show that the B field produces a \pi flux phase for holons, converting them into Dirac--like fermions, while the V field, taking into account the feedback of holons produces a gap for spinons vanishing in zero doping limit. The nonlinear sigma-model with a mass term describes the crossover from short-ranged antiferromagnetic (AF) state in doped samples to long range AF order in reference compounds. Moreover, we derive a low--energy effective action in terms of spinons, holons and a self-generated U(1) gauge field. The gauge fluctuations are not confining due to coupling to holons, but yield an attractive interaction between spinons and holons leading to a bound state with electron quantum numbers. The renormalisation effects due to gauge fluctuations give rise to non--Fermi liquid behaviour for the composite electron.This formalism provides a new interpretation of the spin gap in underdoped superconductors (due to short-ranged AF order) and predicts the minimal gap for the physical electron is proportional to the square root of the doping concentration.Comment: 31 pages, REVTEX, to be published in Phys. Rev.