Quantum to Classical Transition in a Single-Ion Laser

Stimulated emission of photons from a large number of atoms into the mode of a strong light field is the principle mechanism for lasing in "classical" lasers. The onset of lasing is marked by a threshold which can be characterised by a sharp increase in photon flux as a function of external pumping strength. The same is not necessarily true for the fundamental building block of a laser: a single trapped atom interacting with a single optical radiation mode. It has been shown that such a "quantum" laser can exhibit thresholdless lasing in the regime of strong coupling between atom and radiation field. However, although theoretically predicted, a threshold at the single-atom level could not be experimentally observed so far. Here, we demonstrate and characterise a single-atom laser with and without threshold behaviour by changing the strength of atom-light field coupling. We observe the establishment of a laser threshold through the accumulation of photons in the optical mode even for a mean photon number substantially lower than for the classical case. Furthermore, self-quenching occurs for very strong external pumping and constitutes an intrinsic limitation of single-atom lasers. Moreover, we find that the statistical properties of the emitted light can be adjusted for weak external pumping, from the quantum to the classical domain. Our observations mark an important step towards fundamental understanding of laser operation in the few-atom limit including systems based on semiconductor quantum dots or molecules.Comment: 19 pages, 4 figures, 10 pages supplement, accepted by Nature Physic

Out-of-equilibrium physics in driven dissipative coupled resonator arrays

Coupled resonator arrays have been shown to exhibit interesting many- body physics including Mott and Fractional Hall states of photons. One of the main differences between these photonic quantum simulators and their cold atoms coun- terparts is in the dissipative nature of their photonic excitations. The natural equi- librium state is where there are no photons left in the cavity. Pumping the system with external drives is therefore necessary to compensate for the losses and realise non-trivial states. The external driving here can easily be tuned to be incoherent, coherent or fully quantum, opening the road for exploration of many body regimes beyond the reach of other approaches. In this chapter, we review some of the physics arising in driven dissipative coupled resonator arrays including photon fermionisa- tion, crystallisation, as well as photonic quantum Hall physics out of equilibrium. We start by briefly describing possible experimental candidates to realise coupled resonator arrays along with the two theoretical models that capture their physics, the Jaynes-Cummings-Hubbard and Bose-Hubbard Hamiltonians. A brief review of the analytical and sophisticated numerical methods required to tackle these systems is included.Comment: Chapter that appeared in "Quantum Simulations with Photons and Polaritons: Merging Quantum Optics with Condensed Matter Physics" edited by D.G.Angelakis, Quantum Science and Technology Series, Springer 201

5-a-day fruit and vegetable food product labels: reduced fruit and vegetable consumption following an exaggerated compared to a modest label.

BACKGROUND: Food product labels based on the WHO 5-a-day fruit and vegetable (FV) message are becoming increasingly common, but these labels may impact negatively on complementary or subsequent FV consumption. This study aimed to investigate the impact of a '3 of your 5-a-day' versus a '1 of your 5-a-day' smoothie product label on subsequent FV consumption. METHODS: Using an acute experimental design, 194 participants (90 males, 104 females) were randomised to consume a smoothie labelled as either '3 of your 5-a-day' (N = 97) or '1 of your 5-a-day' (N = 97) in full, following a usual breakfast. Subsequent FV consumption was measured for the rest of the day using 24-h recall. Usual FV consumption was also assessed via 24-h recall for the day before the study. RESULTS: Regression analyses revealed a significantly lower subsequent FV consumption following smoothies displaying the '3 of your 5-a-day' label compared to the '1 of your 5-a-day' label (Beta = - 0.15, p = 0.04). Secondary analyses revealed these effects to be driven mainly by changes to consumption in usual high FV consumers, in females and in vegetable as opposed to fruit consumption. CONCLUSIONS: These findings demonstrate a role for label information in food intake, and the potential negative impacts of an exaggerated food product label on healthy food consumption and healthy dietary profiles

Anti-cancer drug validation: the contribution of tissue engineered models

Abstract Drug toxicity frequently goes concealed until clinical trials stage, which is the most challenging, dangerous and expensive stage of drug development. Both the cultures of cancer cells in traditional 2D assays and animal studies have limitations that cannot ever be unraveled by improvements in drug-testing protocols. A new generation of bioengineered tumors is now emerging in response to these limitations, with potential to transform drug screening by providing predictive models of tumors within their tissue context, for studies of drug safety and efficacy. Considering the NCI60, a panel of 60 cancer cell lines representative of 9 different cancer types: leukemia, lung, colorectal, central nervous system (CNS), melanoma, ovarian, renal, prostate and breast, we propose to review current Bstate of art^ on the 9 cancer types specifically addressing the 3D tissue models that have been developed and used in drug discovery processes as an alternative to complement their studyThis article is a result of the project FROnTHERA (NORTE-01-0145-FEDER-000023), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This article was also supported by the EU Framework Programme for Research and Innovation HORIZON 2020 (H2020) under grant agreement n° 668983 — FoReCaST. FCT distinction attributed to Joaquim M. Oliveira (IF/00423/2012) and Vitor M. Correlo (IF/01214/2014) under the Investigator FCT program is also greatly acknowledged.info:eu-repo/semantics/publishedVersio

Increasing vegetable intakes: rationale and systematic review of published interventions

Purpose While the health benefits of a high fruit and vegetable consumption are well known and considerable work has attempted to improve intakes, increasing evidence also recognises a distinction between fruit and vegetables, both in their impacts on health and in consumption patterns. Increasing work suggests health benefits from a high consumption specifically of vegetables, yet intakes remain low, and barriers to increasing intakes are prevalent making intervention difficult. A systematic review was undertaken to identify from the published literature all studies reporting an intervention to increase intakes of vegetables as a distinct food group. Methods Databases—PubMed, PsychInfo and Medline—were searched over all years of records until April 2015 using pre-specified terms. Results Our searches identified 77 studies, detailing 140 interventions, of which 133 (81 %) interventions were conducted in children. Interventions aimed to use or change hedonic factors, such as taste, liking and familiarity (n = 72), use or change environmental factors (n = 39), use or change cognitive factors (n = 19), or a combination of strategies (n = 10). Increased vegetable acceptance, selection and/or consumption were reported to some degree in 116 (83 %) interventions, but the majority of effects seem small and inconsistent. Conclusions Greater percent success is currently found from environmental, educational and multi-component interventions, but publication bias is likely, and long-term effects and cost-effectiveness are rarely considered. A focus on long-term benefits and sustained behaviour change is required. Certain population groups are also noticeably absent from the current list of tried interventions

Seasonality and Children’s Blood Lead Levels: Developing a Predictive Model Using Climatic Variables and Blood Lead Data from Indianapolis, Indiana, Syracuse, New York, and New Orleans, Louisiana (USA)

On a community basis, urban soil contains a potentially large reservoir of accumulated lead. This study was undertaken to explore the temporal relationship between pediatric blood lead (BPb), weather, soil moisture, and dust in Indianapolis, Indiana; Syracuse, New York; and New Orleans, Louisiana. The Indianapolis, Syracuse, and New Orleans pediatric BPb data were obtained from databases of 15,969, 14,467, and 2,295 screenings, respectively, collected between December 1999 and November 2002, January 1994 and March 1998, and January 1998 and May 2003, respectively. These average monthly child BPb levels were regressed against several independent variables: average monthly soil moisture, particulate matter < 10 μm in diameter (PM(10)), wind speed, and temperature. Of temporal variation in urban children’s BPb, 87% in Indianapolis (R(2) = 0.87, p = 0.0004), 61% in Syracuse (R(2) = 0.61, p = 0.0012), and 59% in New Orleans (R(2) = 0.59, p = 0.0000078) are explained by these variables. A conceptual model of urban Pb poisoning is suggested: When temperature is high and evapotranspiration maximized, soil moisture decreases and soil dust is deposited. Under these combined weather conditions, Pb-enriched PM(10) dust disperses in the urban environment and causes elevated Pb dust loading. Thus, seasonal variation of children’s Pb exposure is probably caused by inhalation and ingestion of Pb brought about by the effect of weather on soils and the resulting fluctuation in Pb loading

Evaluation of clustering algorithms for protein-protein interaction networks

BACKGROUND: Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). RESULTS: A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. CONCLUSION: This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

BACKGROUND: A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. CONCLUSIONS/SIGNIFICANCE: ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple

Multi-Organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) Study

Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n = 66/tissue) and atherosclerotic and unaffected arterial wall (n = 40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n = 15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n = 3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n = 49/48) and one visceral fat (n = 59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P = 0.008 and P = 0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n = 55/54) relating to carotid stenosis (P = 0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n = 16/17, P<10−27and−30). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, by cellular and lesion expression of LDB2, and by the expression of 13 TEML genes in Ldb2–deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and, together with LDB2, merits further attention in CAD research

Perspective-Taking and Willingness to Engage in Intergroup Contact

The current research explored whether perspective-taking increases willingness to engage in contact with stereotyped outgroup members. Across three studies, we find that perspective-taking increases willingness to engage in contact with negatively-stereotyped targets. In Study 1, perspective-takers sat closer to, whereas stereotype suppressors sat further from, a hooligan compared to control participants. In Study 2, individual differences in perspective-taking tendencies predicted individuals' willingness to engage in contact with a hooligan, having effects above and beyond those of empathic concern. Finally, Study 3 demonstrated that perspective-taking's effects on intergroup contact extend to the target's group (i.e., another homeless man), but not to other outgroups (i.e., a man of African descent). Consistent with other perspective-taking research, our findings show that perspective-taking facilitates the creation of social bonds by increasing contact with stereotyped outgroup members