Behavioral/Cognitive Acute and Long-Term Suppression of Feeding Behavior by POMC Neurons in the Brainstem and Hypothalamus, Respectively

POMC-derived melanocortins inhibit food intake. In the adult rodent brain, POMC-expressing neurons are located in the arcuate nucleus (ARC) and the nucleus tractus solitarius (NTS), but it remains unclear how POMC neurons in these two brain nuclei regulate feeding behavior and metabolism differentially. Using pharmacogenetic methods to activate or deplete neuron groups in separate brain areas, in the present study, we show that POMC neurons in the ARC and NTS suppress feeding behavior at different time scales. Neurons were activated using the DREADD (designer receptors exclusively activated by designer drugs) method. The evolved human M3-muscarinic receptor was expressed in a selective population of POMC neurons by stereotaxic infusion of Cre-recombinase–dependent, adenoassociated virus vectors into the ARC or NTS of POMC-Cre mice. After injection of the human M3-muscarinic receptor ligand clozapine-N-oxide (1 mg/kg, i.p.), acute activation of NTS POMC neurons produced an immediate inhibition of feeding behavior. In contrast, chronic stimulation was required for ARC POMC neurons to suppress food intake. Using adeno-associated virus delivery of the diphtheria toxin receptor gene, we found that diphtheria toxin–induced ablation of POMC neurons in the ARC but not the NTS, increased food intake, reduced energy expenditure, and ultimately resulted in obesity and metabolic and endocrine disorders. Our results reveal different behavioral functions of POMC neurons in the ARC and NTS, suggesting that POMC neurons regulate feeding and energy homeostasis by integrating long-term adiposity signals from the hypothalamus and short-term satiety signals from the brainstem

EVALUATION THE EXPRESSION OF THREE GENES TO EPITHELIAL OVARIAN CANCER RISK IN CHINESE POPULATION

Background: Ovarian cancer is associated with poor survival, because patients are diagnosed at an advanced stage of the disease, and in addition, tumors develop chemoresistance, which carries a poor prognosis for the patient. Material and methods: We hypothesize that high expression of SDF-1, survivin and smac is associated with ovarian cancers development and could be used as a biomarker to identify this disease. The expressions of SDF-1, survivin and smac in normal ovarian (NO) tissue, benign tumor (BT) tissue and epithelial ovarian cancer (EOC) tissue were immunohistochemically analysed. Results: Positive expressions of SDF-1, survivin and smac were significantly higher in EOC tissue than those in NO and BT tissues. SDF-1 expressions were significantly more weaker in advanced ovarian carcinomas (FIGO stage III–IV), and in high-grade carcinomas. There was a positive correlation between EOC patients with lymph node metastasis and with ascites and SDF-1 positivity (P < 0.05). Survivin expressions were significantly more stronger in advanced ovarian carcinomas (FIGO stage III–IV), and in high-grade carcinomas. There was a positive correlation between EOC patients with lymph node metastasis and with ascites and surviving positivity (P < 0.05). Smac expressions were significantly more stronger in advanced ovarian carcinomas (FIGO stage III–IV), and in high-grade carcinomas. There was a positive correlation between EOC patients with lymph node metastasis and with ascites and smac positivity (P < 0.05)

Increases in solar conversion efficiencies of the ZrO2 nanofiber-doped TiO2 photoelectrode for dye-sensitized solar cells

In this paper, in order to improve the efficiency of dye-sensitized solar cells, we introduced zirconia [ZrO2] nanofibers into a mesoporous titania [TiO2] photoelectrode. The photoelectrode consists of a few weight percent of ZrO2 nanofibers and a mesoporous TiO2 powder. The mixed ZrO2 nanofibers and the mesoporous TiO2 powder possessed a larger surface area than the corresponding mesoporous TiO2 powder. The optimum ratio of the ZrO2 nanofiber was 5 wt.%. The 5 wt.% ZrO2-mixed device could get a short-circuit photocurrent density of 15.9 mA/cm2, an open-circuit photovoltage of 0.69 V, a fill factor of 0.60, and a light-to-electricity conversion efficiency of 6.5% under irradiation of AM 1.5 (100 mW/cm2)

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at s√=8 TeV with the ATLAS detector

The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1fb−1 of proton–proton collision data at √s = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via t˜→tχ˜01 or t˜→ bχ˜±1 →bW(∗)χ˜01 , where χ˜01 (χ˜±1 ) denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of t˜ → tχ˜01 . For a branching fraction of 100%, top squark masses in the range 270–645 GeV are excluded for χ˜01 masses below 30 GeV. For a branching fraction of 50% to either t˜ → tχ˜01 or t˜ → bχ˜±1 , and assuming the χ˜±1 mass to be twice the χ˜01 mass, top squark masses in the range 250–550 GeV are excluded for χ˜01 masses below 60 GeV

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure