A Climate for Change in the UN Security Council? Member States' Approaches to the Climate-Security Nexus

This research report is the first to systematically engage with the growing political agenda of the climate-security nexus and to place a particular focus on the relationship between the state and the only international organ with a mandate to maintain international peace and security: the United Nations Security Council (UNSC). Discussions that have been ongoing since 2007, scattered governmental positions and the difficulty of achieving an overview of the various understandings, topics, concerns and responses of the UNSC member states in relation to the climate-security nexus all indicate a need to address this topic. This report therefore assesses and maps if and how the UNSC members acknowledge the linkages between climate change and security and how they position themselves with respect to these debates in the UNSC. With a large international network of interdisciplinary and country-specialized partner scientists, the analysis relies on an extensive spectrum of official primary sources from member state governments, various ministry strategies (such as those addressing security and climate change), UNSC documents and interdisciplinary academic literature on the climate-security nexus. It is located in the context of substantiated planetary climate emergencies and existential threats as well as urgent calls for action from the UN and member state representatives, scientific networks in Earth System Sciences and youth protests. Based on broad empirical research findings, this report concludes that all 15 current UNSC member states acknowledge the climate-security nexus in complex, changing and partly country-dependent ways. The report formulates an outlook and recommendations for decision-makers and scholars with a particular focus on strengthening the science-policy interface and dialogue and emphasizing the urgent need for institutional, multilateral and scientifically informed change. It also illustrates how essential it is for the UNSC to recognize and adapt institutional working methods to the interrelations of climate change and security and their effects as a cross-cutting issue

Roles of Gβγ in membrane recruitment and activation of p110γ/p101 phosphoinositide 3-kinase γ

Receptor-regulated class I phosphoinositide 3-kinases (PI3K) phosphorylate the membrane lipid phosphatidylinositol (PtdIns)-4,5-P2 to PtdIns-3,4,5-P3. This, in turn, recruits and activates cytosolic effectors with PtdIns-3,4,5-P3–binding pleckstrin homology (PH) domains, thereby controlling important cellular functions such as proliferation, survival, or chemotaxis. The class IB p110γ/p101 PI3Kγ is activated by Gβγ on stimulation of G protein–coupled receptors. It is currently unknown whether in living cells Gβγ acts as a membrane anchor or an allosteric activator of PI3Kγ, and which role its noncatalytic p101 subunit plays in its activation by Gβγ. Using GFP-tagged PI3Kγ subunits expressed in HEK cells, we show that Gβγ recruits the enzyme from the cytosol to the membrane by interaction with its p101 subunit. Accordingly, p101 was found to be required for G protein–mediated activation of PI3Kγ in living cells, as assessed by use of GFP-tagged PtdIns-3,4,5-P3–binding PH domains. Furthermore, membrane-targeted p110γ displayed basal enzymatic activity, but was further stimulated by Gβγ, even in the absence of p101. Therefore, we conclude that in vivo, Gβγ activates PI3Kγ by a mechanism assigning specific roles for both PI3Kγ subunits, i.e., membrane recruitment is mediated via the noncatalytic p101 subunit, and direct stimulation of Gβγ with p110γ contributes to activation of PI3Kγ

Normalization by Evaluation for Call-by-Push-Value and Polarized Lambda-Calculus

We observe that normalization by evaluation for simply-typed lambda-calculus with weak coproducts can be carried out in a weak bi-cartesian closed category of presheaves equipped with a monad that allows us to perform case distinction on neutral terms of sum type. The placement of the monad influences the normal forms we obtain: for instance, placing the monad on coproducts gives us eta-long beta-pi normal forms where pi refers to permutation of case distinctions out of elimination positions. We further observe that placing the monad on every coproduct is rather wasteful, and an optimal placement of the monad can be determined by considering polarized simple types inspired by focalization. Polarization classifies types into positive and negative, and it is sufficient to place the monad at the embedding of positive types into negative ones. We consider two calculi based on polarized types: pure call-by-push-value (CBPV) and polarized lambda-calculus, the natural deduction calculus corresponding to focalized sequent calculus. For these two calculi, we present algorithms for normalization by evaluation. We further discuss different implementations of the monad and their relation to existing normalization proofs for lambda-calculus with sums. Our developments have been partially formalized in the Agda proof assistant

Normalization by evaluation for call-by-push-value and polarized lambda calculus

We observe that normalization by evaluation for simply-typed lambda-calculus with weak coproducts can be carried out in a weak bi-cartesian closed category of presheaves equipped with a monad that allows us to perform case distinction on neutral terms of sum type. The placement of the monad influences the normal forms we obtain: for instance, placing the monad on coproducts gives us eta-long beta-pi normal forms where pi refers to permutation of case distinctions out of elimination positions. We further observe that placing the monad on every coproduct is rather wasteful, and an optimal placement of the monad can be determined by considering polarized simple types inspired by focalization. Polarization classifies types into positive and negative, and it is sufficient to place the monad at the embedding of positive types into negative ones. We consider two calculi based on polarized types: pure call-by-push-value (CBPV) and polarized lambda-calculus, the natural deduction calculus corresponding to focalized sequent calculus. For these two calculi, we present algorithms for normalization by evaluation. We further discuss different implementations of the monad and their relation to existing normalization proofs for lambda-calculus with sums. Our developments have been partially formalized in the Agda proof assistant

Characterisation of ascocorynin biosynthesis in the purple jellydisc fungus Ascocoryne sarcoides

Background: Non-ribosomal peptide synthetase-like (NRPS-like) enzymes are highly enriched in fungal genomes and can be discriminated into reducing and non-reducing enzymes. Non-reducing NRPS-like enzymes possess a C-terminal thioesterase domain that catalyses the condensation of two identical aromatic α-keto acids under the formation of enzyme-specific substrate-interconnecting core structures such as terphenylquinones, furanones, butyrolactones or dioxolanones. Ascocoryne sarcoides produces large quantities of ascocorynin, which structurally resembles a terphenylquinone produced from the condensation of p-hydroxyphenylpyruvate and phenylpyruvate. Since the parallel use of two different substrates by a non-reducing NRPS-like enzyme appeared as highly unusual, we investigated the biosynthesis of ascocorynin in A. sarcoides. Results: Here, we searched the genome of A. sarcoides for genes coding for non-reducing NRPS-like enzymes. A single candidate gene was identified that was termed acyN. Heterologous gene expression confirmed that AcyN is involved in ascocorynin production but only produces the non-hydroxylated precursor polyporic acid. Although acyN is embedded in an ascocorynin biosynthesis gene cluster, a gene encoding a monooxygenase required for the hydroxylation of polyporic acid was not present. Expression analyses of all monooxygenase-encoding genes from A. sarcoides identified a single candidate that showed the same expression pattern as acyN. Accordingly, heterologous co-expression of acyN and the monooxygenase gene resulted in the production of ascocorynin. Structural modelling of the monooxygenase suggests that the hydrophobic substrate polyporic acid enters the monooxygenase from a membrane facing entry site and is converted into the more hydrophilic product ascocorynin, which prevents its re-entry for a second round of hydroxylation. Conclusion: This study characterises the first naturally occurring polyporic acid synthetase from an ascomycete. It confirms the high substrate and product specificity of this non-reducing NRPS-like enzyme and highlights the requirement of a monooxygenase to produce the terphenylquinone ascocorynin

Determinants of Stock Market Volatility and Risk Premia

Summary. We show the dynamics of diverse beliefs is the primary propagation mechanism of volatility in asset markets. Hence, we treat the characteristics of the market beliefs as a primary, primitive, explanation of market volatility. We study an economy with stock and riskless bond markets and formulate a financial equilibrium model with diverse and time varying beliefs. Agents' states of belief play a key role in the market, requiring an endogenous expansion of the state space. To forecast prices agents must forecast market states of belief which are beliefs of "others" hence our equilibrium embodies the Keynes "Beauty Contest." A "market state of belief" is a vector which uniquely identifies the distribution of conditional probabilities of agents. Restricting beliefs to satisfy the rationality principle of Rational Belief (see Keywords: Market states of beliefs; market volatility; equity risk premium; riskless rate; over confidence; Heterogenous beliefs; Rational Belief; optimism; pessimism; empirical distribution. JEL Classification Numbers: G1; G12; E43; E44; D58; D84. Introduction The forces which determine equilibrium market volatility and risk premia are probably the most debated topics in the analysis of financial markets. The debate is driven, in part, by empirical evidence of market "anomalies" which have challenged students of the subject. Consumptionbased asset pricing theory has had a profound impact on our view of financial markets. Early work of Difficult to account for risk premia are not confined to the consumption based asset pricing theory; they arise in many asset pricing models. Three examples will illustrate. The Expectations Hypothesis is rejected by most studies of the term structure (e.g. Backus, We suggest market volatility and risk premia are primarily determined by the structure of agents' expectations called "market state of belief." Diversity and dynamics of beliefs are then the root cause of price volatility and the key factor explaining risk premia. Agents may be "bulls" or "bears." A bull at date t expects the date t+1 rate of return on investments to be higher than normal, where "normal" is defined by the empirical distribution of past returns. Date t bears expect returns at t+1 to be lower than normal. Agents do not hold Rational Expectations (in short RE) since the environment is dynamically changing, non stationary, and true probabilities are unknown to anyone. In such complex environment agents use subjective models. Some consider these agents irrational, but one cannot require them to know what they cannot know: there is a wide gulf between an RE agent and irrational behavior. We explore the structure of economies with diverse beliefs and show they must have an expanded state space. Our computing models assume agents hold rational beliefs in accord with the theory of Rational Belief Equilibrium (in short, RBE) due to The Rational Belief Principle. "Rational Belief" (in short, RB) is not a theory that demonstrates rational agents should adopt any particular belief. Indeed, since the RB theory explains the observed belief heterogeneity, it would be a contradiction to propose that a particular belief is the "correct" belief agents should adopt. The RB theory starts by observing that the true stochastic law 3 of motion of the economy is non-stationary with structural breaks and complex dynamics hence the probability law of the process is not known. Agents have a long history of past data generated by the process which they use to compute relative frequencies of finite dimensional events hence all finite moments. With this knowledge they compute the empirical distribution and use it to construct an empirical probability measure over sequences. In contrast with a Rational Expectations Equilibrium (in short, REE) where the true law of motion is known, agents in an RBE who do not know the truth, form subjective beliefs based only on observed data. Hence, any principle on the basis of which agents can be judged as rational must be based only on the data. Since a "belief" is a model of the economy together with a probability Earlier papers using the RBE rationality principle have also argued that agents' beliefs are central to explaining market volatility (e.g. Motolese The Main Results . First, "Belief states" are developed as a tool for equilibria with diverse belief. Next, our method is to use properties of market beliefs as primitive explanation of volatility. Two characteristics of beliefs fully account for all features of volatility and premia observed in markets: intensity of fat tails in the belief densities of agents; (B) asymmetry in the proportion of bull and bear states in the market over time. High intensity means agents exhibit rationalizable over confidence with fat tails in their subjective densities. Asymmetry in frequency of belief is a characteristic which says that on average, at more than half of the time agents do not expect to make excess returns. Our model also implies market returns must exhibit stochastic volatility which is generated by the dynamics of market belief. The Economic Environment The economy has two types of agents and a large number of identical agents within each type. An agent is a member of one of the two types of infinitely lived dynasties identified by their endowment , utility ( defined over consumption) and by their belief. A dynasty member lives a fixed short life and during his life makes decisions based on his own belief without knowing the states of belief of his predecessors. He is replaced by an identical member. There are two assets: a stock and a riskless, one period, bond. There is an aggregate output process which is Y t , t ' 1, 2, . . . divided between dividends paid to owners of the common stocks and non -D t , t ' 1, 2, . . . dividend endowment which is paid to the agents. The dividend process is described by 1 A Stable Process is defined in where { , t = 1, 2, ...} is a stochastic process under a true probability which is non-stationary with x t structural breaks and time dependent distribution. This time varying probability is not known by any agent and is not specified. Instead, we assume { , t = 1, 2, ...} is a stable process 1 hence it has x t an empirical distribution which is known to all agents who learn it from the data. This empirical distribution is represented by a stationary Markov process, with a year as a unit of time, defined by 2 (2) with x ) The infinitely lived agents are enumerated j = 1, 2 and we use the following notation: -consumption of j at t; C j t -amount of stock purchased by j at t; θ j t -amount of one period bond purchased at discount by agent j at t; B j t -stock price at date t; q s t -the discount price of a one period bond at t; q b t -non capital income of agent j at date t; Λ j t H t -information at t, recording the history of all observables up to t. Given probability belief , agent j selects portfolio and consumption plans to solve the problem Q j t (3a) Max We assume additively separable, power utility over consumption, a model that failed to generate premia in other studies (see, Campbell and Cochrane (2000) The Euler equations are and the market clearing conditions are then A Rational Expectations Equilibrium (REE) Strictly speaking we cannot evaluate the REE since the true output process has not been specified. We thus define an REE to be the economy in which all agents believe that 4 when C t approaches the mean of past consumption? Campbell and Cochrane (1999, page 244) show that for the model to generate the desired moments, the degree of risk aversion is 80 at steady state and exceeds 300 frequently along any time path. If instead we use Abel's (1990), (1999) formulation , marginal utility is normalized to be 1 at habit but the model does not generate volatility. Second, big fluctuations of stock prices are observed during long periods when consumption grows smoothly as was the case during the volatile period of 1992 -2002. Finally, the model predicts perfect correlation between consumption growth and stock returns but the record (see 7 price\dividend ratio , (ii) the risky return R, (iii) the riskless rate ; the equity premium , the q s e p Sharp Ratio and the correlation between x and R. Moments of market data vary with sources reporting and methods of estimation. The market data reported in The "Equity Premium" is not the only puzzle; the wider question is how to explain market volatility. The General Structure of Equilibria with Diverse and Time Dependent Beliefs Although Our methodology is to use the distribution of beliefs to explain market volatility hence we need to determine a level of detail at which agents "justify" their beliefs. If we aim at a complete specification of such modeling, our study is doomed to be bogged down in details of inference from small samples and information processing. Although interesting, from a general equilibrium perspective it is not needed. To study volatility we focus on a narrow but operational question. Since (4a)-(4b) require specification of conditional probabilities, we need only a tractable way to describe differences among agents' beliefs and time variability of their conditional probabilities, without fully specified models to justify them. From our point of view what matters is the fact that market beliefs are diverse and time dependent; the reasoning which lead agents to the subjective models are secondary. The tool we developed for this goal is the individual and the market "state of belief" which we now explain. Market States of Belief and Anonymity: Expansion of the State Space The usual state space for agent j is denoted by S j but when beliefs change over time we introduce an additional state variable called "agent j state of belief ." It is a variable generated by agent j, expressing his date t subjective view of the future and denoted by . It has the g j t 0 G j property that once specified, the conditional probability function of an agent is uniquely specified and 5 Note that larger values of g imply a more bearish perspective. In the applications below larger g will express an agent's reduced probability belief in making excess returns. This may appear unnatural but we study equilibria with asymmetry measured by the frequency at which an agent is a bull or a bear. One of our main results says that the data supports a model where, on average, agents expect to make excess returns less than 50% of the time or, equivalently, that in a large market a majority of agents are pessimistic about making excess returns. We thus focus on the market pessimists. Since in the computational model we use a logistic function to express this asymmetry, it turns out that the use of a logistic function necessitates the condition that a larger g means more bearishness. Without the desired asymmetry g could have an opposite interpretation. We discuss this point further in Appendix A. is actually a proxy for j's conditional probability function. We note g j t that are privately perceived by agent j and have meaning only to him. Since a dynasty consists of g j t a sequence of decision makers, used by j has no impact on the description of beliefs by other g j t dynasty members. In the model of this paper agents forecast dividend or profit growth rate x t+1 (i.e. the exogenous variable) hence describes agent j conditional probability of profit growth at g j t 0 ú t+1. We shall permit rational agents to be "bulls" who are optimistic about future excess returns or "bears" who are pessimistic about future excess returns. To understand the role of we introduce g he is a bull and makes g j t < a higher profit growth forecasts than the ones implied by As indicated, we do not explain the reasoning used by agents to deviate from the empirical forecast. It is a common practice among forecasters to use the strict econometric forecast only as a benchmark. Given such benchmark, a forecaster uses his own model to add a component reflecting an evaluation of circumstances at a date t that call for a deviation at t from the benchmark. In short, is a description of how the model of agent j deviates from the statistical forecast implied by (2). g j t In this paper we assume that at any date the state of belief is a realization of a process of the form Persistent states of belief which depends upon current market data fit different cases of economies with diverse beliefs. We consider three examples to illustrate how one may think about them. agents to assume they cannot affect endogenous variables. It is analogous to requiring a competitive firm to assume it has no effect on prices. The issue here is the specification of how agents forecast prices. To that end we define the "market state of belief " as a vector , keeping in z t 11 is so central to our approach that we use three notational devices to highlight it: We now return to the economy with two agent types and simplify by assuming the market belief is observable. This assumption is entirely reasonable since there is a vast amount of (z 1 t , z 2 t ) public data on the distribution of forecasts in the market and on the dynamics of this distribution. Indeed, using forecast data obtained from the Blue Chip Economic Indicators and the Survey of Professional Forecasters we constructed various measures of market states of belief. Since (z 1 t , z 2 t ) is observable we need to modify the empirical distribution (2) and include in it. We assume (z In any application one assumes the parameters of We then estimated principal components to handle multitude of forecasted variables (for details, see Fan % Σ Finally, denote by m the probability measure on infinite sequences implied by (8) with the invariant distribution as the initial distribution. We then write where H t is the history at t. E m ( w t%1 | H t

Lung cancer in HIV patients and their parents: A Danish cohort study

<p>Abstract</p> <p>Background</p> <p>HIV patients are known to be at increased risk of lung cancer but the risk factors behind this are unclear.</p> <p>Methods</p> <p>We estimated the cumulative incidence and relative risk of lung cancer in 1) a population of all Danish HIV patients identified from the Danish HIV Cohort Study (n = 5,053) and a cohort of population controls matched on age and gender (n = 50,530) (study period; 1995 - 2009) and 2) their parents (study period; 1969 - 2009). Mortality and relative risk of death after a diagnosis of lung cancer was estimated in both populations.</p> <p>Results</p> <p>29 (0.6%) HIV patients vs. 183 (0.4%) population controls were diagnosed with lung cancer in the observation period. HIV patients had an increased risk of lung cancer (adjusted incidence rate ratio (IRR); 2.38 (95% CI; 1.61 - 3.53)). The IRR was considerably increased in HIV patients who were smokers or former smokers (adjusted IRR; 4.06 (95% CI; 2.66 - 6.21)), male HIV patients with heterosexual route of infection (adjusted IRR; 4.19 (2.20 - 7.96)) and HIV patients with immunosuppression (adjusted IRR; 3.25 (2.01 - 5.24)). Both fathers and mothers of HIV patients had an increased risk of lung cancer (adjusted IRR for fathers; 1.31 (95% CI: 1.09 - 1.58), adjusted IRR for mothers 1.35 (95% CI: 1.07 - 1.70)). Mortality after lung cancer diagnose was increased in HIV patients (adjusted mortality rate ratio 2.33 (95%CI; 1.51 - 3.61), but not in the parents. All HIV patients diagnosed with lung cancer were smokers or former smokers.</p> <p>Conclusion</p> <p>The risk was especially increased in HIV patients who were smokers or former smokers, heterosexually infected men or immunosuppressed. HIV appears to be a marker of behavioural or family related risk factors that affect the incidence of lung cancer in HIV patients.</p

Secondary organic aerosol (SOA) yields from NO_3 radical + isoprene based on nighttime aircraft power plant plume transects

Nighttime reaction of nitrate radicals (NO_3) with biogenic volatile organic compounds (BVOC) has been proposed as a potentially important but also highly uncertain source of secondary organic aerosol (SOA). The southeastern United States has both high BVOC and nitrogen oxide (NO_x) emissions, resulting in a large model-predicted NO_3-BVOC source of SOA. Coal-fired power plants in this region constitute substantial NO_x emissions point sources into a nighttime atmosphere characterized by high regionally widespread concentrations of isoprene. In this paper, we exploit nighttime aircraft observations of these power plant plumes, in which NO_3 radicals rapidly remove isoprene, to obtain field-based estimates of the secondary organic aerosol yield from NO_3+isoprene. Observed in-plume increases in nitrate aerosol are consistent with organic nitrate aerosol production from NO_3+isoprene, and these are used to determine molar SOA yields, for which the average over nine plumes is 9% (±5%). Corresponding mass yields depend on the assumed molecular formula for isoprene-NO_3-SOA, but the average over nine plumes is 27% (±14%), on average larger than those previously measured in chamber studies (12%–14% mass yield as ΔOA∕ΔVOC after oxidation of both double bonds). Yields are larger for longer plume ages. This suggests that ambient aging processes lead more effectively to condensable material than typical chamber conditions allow. We discuss potential mechanistic explanations for this difference, including longer ambient peroxy radical lifetimes and heterogeneous reactions of NO_3-isoprene gas phase products. More in-depth studies are needed to better understand the aerosol yield and oxidation mechanism of NO_3 radical+isoprene, a coupled anthropogenic–biogenic source of SOA that may be regionally significant

Secondary organic aerosol (SOA) yields from NO3 radical + isoprene based on nighttime aircraft power plant plume transects

Nighttime reaction of nitrate radicals (NO3) with biogenic volatile organic compounds (BVOC) has been proposed as a potentially important but also highly uncertain source of secondary organic aerosol (SOA). The southeastern United States has both high BVOC and nitrogen oxide (NOx) emissions, resulting in a large model-predicted NO3-BVOC source of SOA. Coal-fired power plants in this region constitute substantial NOx emissions point sources into a nighttime atmosphere characterized by high regionally widespread concentrations of isoprene. In this paper, we exploit nighttime aircraft observations of these power plant plumes, in which NO3 radicals rapidly remove isoprene, to obtain field-based estimates of the secondary organic aerosol yield from NO3+isoprene. Observed in-plume increases in nitrate aerosol are consistent with organic nitrate aerosol production from NO3+isoprene, and these are used to determine molar SOA yields, for which the average over nine plumes is 9% (±5%). Corresponding mass yields depend on the assumed molecular formula for isoprene-NO3-SOA, but the average over nine plumes is 27% (±14%), on average larger than those previously measured in chamber studies (12%–14% mass yield as ΔOA∕ΔVOC after oxidation of both double bonds). Yields are larger for longer plume ages. This suggests that ambient aging processes lead more effectively to condensable material than typical chamber conditions allow. We discuss potential mechanistic explanations for this difference, including longer ambient peroxy radical lifetimes and heterogeneous reactions of NO3-isoprene gas phase products. More in-depth studies are needed to better understand the aerosol yield and oxidation mechanism of NO3 radical+isoprene, a coupled anthropogenic–biogenic source of SOA that may be regionally significant