Cognitive impairment induced by delta9-tetrahydrocannabinol occurs through heteromers between cannabinoid CB1 and serotonin 5-HT2A receptors

Delta-9-tetrahydrocannabinol (THC), the main psychoactive compound of marijuana, induces numerous undesirable effects, including memory impairments, anxiety, and dependence. Conversely, THC also has potentially therapeutic effects, including analgesia, muscle relaxation, and neuroprotection. However, the mechanisms that dissociate these responses are still not known. Using mice lacking the serotonin receptor 5-HT2A, we revealed that the analgesic and amnesic effects of THC are independent of each other: while amnesia induced by THC disappears in the mutant mice, THC can still promote analgesia in these animals. In subsequent molecular studies, we showed that in specific brain regions involved in memory formation, the receptors for THC and the 5-HT2A receptors work together by physically interacting with each other. Experimentally interfering with this interaction prevented the memory deficits induced by THC, but not its analgesic properties. Our results highlight a novel mechanism by which the beneficial analgesic properties of THC can be dissociated from its cognitive side effects

A phase II study of sequential 5-fluorouracil, epirubicin and cyclophosphamide (FEC) and paclitaxel in advanced breast cancer (Protocol PV BC 97/01)

Sequential administration of the association of 5-fluorouracil, epirubicin and cyclophosphamide (FEC) and paclitaxel could be better tolerated than the association of an anthracycline and paclitaxel while having a similar antitumour effect. 69 patients with advanced breast cancer previously untreated with anthracyclines or paclitaxel entered a phase II multicentre study in which FEC was followed by paclitaxel. Both regimens were administered 4 times every 21 days. The median follow-up is 20 months and 38/69 patients have died. Grade III–IV toxicity was acceptable. Leukopenia occurred in 26% of patients, thrombocytopenia in 2% and anaemia in 4%. One patient had reversible heart failure during FEC therapy. Peripheral neuropathy and arthralgia-myalgia occurred in 9% and 4% of patients, respectively and one patient had respiratory hypersensitivity during paclitaxel treatment. 9 patients did not complete therapy because of: treatment refusal (n= 1), cardiac toxicity (n= 1), early death during FEC chemotherapy (n= 1), major protocol violations (n= 4), hypersensitivity reaction (n= 1) and early death during paclitaxel chemotherapy (n= 1). The overall response rate was 65% (95% CI = 53–76), and 7% of patients had stable disease. Therapy was defined as having failed in 28% of patients because they were not evaluable (13%) or had progressive disease (15%). The median time to progression and survival are 13.2 and 23.5 months, respectively. Sequential FEC-paclitaxel is a suitable strategy for patients with metastatic breast cancer who have not been previously treated with anthracyclines and/or taxanes. In fact, it avoids major haematologic toxicity and has a good antitumour effect. © 2001 Cancer Research Campaign http://www.bjcancer.co

Bright light-emitting diodes based on organometal halide perovskite.

Solid-state light-emitting devices based on direct-bandgap semiconductors have, over the past two decades, been utilized as energy-efficient sources of lighting. However, fabrication of these devices typically relies on expensive high-temperature and high-vacuum processes, rendering them uneconomical for use in large-area displays. Here, we report high-brightness light-emitting diodes based on solution-processed organometal halide perovskites. We demonstrate electroluminescence in the near-infrared, green and red by tuning the halide compositions in the perovskite. In our infrared device, a thin 15 nm layer of CH3NH3PbI(3-x)Cl(x) perovskite emitter is sandwiched between larger-bandgap titanium dioxide (TiO2) and poly(9,9'-dioctylfluorene) (F8) layers, effectively confining electrons and holes in the perovskite layer for radiative recombination. We report an infrared radiance of 13.2 W sr(-1) m(-2) at a current density of 363 mA cm(-2), with highest external and internal quantum efficiencies of 0.76% and 3.4%, respectively. In our green light-emitting device with an ITO/PEDOT:PSS/CH3NH3PbBr3/F8/Ca/Ag structure, we achieved a luminance of 364 cd m(-2) at a current density of 123 mA cm(-2), giving external and internal quantum efficiencies of 0.1% and 0.4%, respectively. We show, using photoluminescence studies, that radiative bimolecular recombination is dominant at higher excitation densities. Hence, the quantum efficiencies of the perovskite light-emitting diodes increase at higher current densities. This demonstration of effective perovskite electroluminescence offers scope for developing this unique class of materials into efficient and colour-tunable light emitters for low-cost display, lighting and optical communication applications.This is the author accepted manuscript and will be under embargo until 3/2/15. The final version is published in Nature Nanotechnology: http://www.nature.com/nnano/journal/vaop/ncurrent/full/nnano.2014.149.html

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p

HAB Buoy: a new instrument for in situ monitoring and early warning of harmful algal bloom events

A new microplankton imaging and analysis instrument, HAB Buoy, is described. It integrates a high-speed camera for in-flow image acquisition with automatic specimen labelling software, known as DiCANN (DinoflagellateCategorisation by Artificial Neural Network). Some preliminary results are presented together with a rationale for its use