Coast-to-Interior Gradient in Recent Northwest Greenland Precipitation Trends (1952–2012)

The spatial and temporal variability of precipitation on the Greenland ice sheet is an essential component of surface mass balance, which has been declining in recent years with rising temperatures. We present an analysis of precipitation trends in northwest (NW) Greenland (1952–2012) using instrumental (coastal meteorological station) and proxy records (snow pits and ice cores) to characterize the precipitation gradient from the coast to the ice sheet interior. Snow-pit-derived precipitation near the coast (1950–2000) has increased (~7% decade−1, p \u3c 0.01) whereas there is no significant change observed in interior snow pits. This trend holds for 1981–2012, where calculated precipitation changes decrease in magnitude with increasing distance from the coast: 13% decade−1 (2.4 mm water equivalent (w.e.) decade−2) at coastal Thule air base (AB), 8.6% decade−1 (4.7 mm w.e. decade−2) at the 2Barrel ice core site 150 km from Thule AB, −5.2% decade−1 (1.7 mm w.e. decade−2) at Camp Century located 205 km from Thule AB, and 4.4% decade−1 (1.0 mm w.e. decade−2) at B26 located 500 km from Thule AB. In general, annually averaged precipitation and annually and seasonally averaged mean air temperatures observed at Thule AB follow trends observed in composite coastal Greenland time series, with both notably indicating winter as the fastest warming season in recent periods (1981–2012). Trends (1961–2012) in seasonal precipitation differ, specifically with NW Greenland summer precipitation increasing (~0.6 mm w.e. decade−2) in contrast with decreasing summer precipitation in the coastal composite time series (3.8 mm w.e. decade−2). Differences in precipitation trends between NW Greenland and coastal composite Greenland underscore the heterogeneity in climate influences affecting precipitation. In particular, recent (1981–2012) changes in NW Greenland annual precipitation are likely a response to a weakening North Atlantic oscillation

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Structure Learning in Human Sequential Decision-Making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. To test whether people exhibit structure learning, we performed experiments involving a mixture of one-armed and two-armed bandit reward models, where structure learning produces many of the qualitative behaviors deemed suboptimal in previous studies. Our results demonstrate humans can perform structure learning in a near-optimal manner

The Origins of Concentric Demyelination: Self-Organization in the Human Brain

Baló's concentric sclerosis is a rare atypical form of multiple sclerosis characterized by striking concentric demyelination patterns. We propose a robust mathematical model for Baló's sclerosis, sharing common molecular and cellular mechanisms with multiple sclerosis. A reconsideration of the analogies between Baló's sclerosis and the Liesegang periodic precipitation phenomenon led us to propose a chemotactic cellular model for this disease. Rings of demyelination appear as a result of self-organization processes, and closely mimic Baló lesions. According to our results, homogeneous and concentric demyelinations may be two different macroscopic outcomes of a single fundamental immune disorder. Furthermore, in chemotactic models, cellular aggressivity appears to play a central role in pattern formation

Adaptative Potential of the Lactococcus Lactis IL594 Strain Encoded in Its 7 Plasmids

The extrachromosomal gene pool plays a significant role both in evolution and in the environmental adaptation of bacteria. The L. lactis subsp. lactis IL594 strain contains seven plasmids, named pIL1 to pIL7, and is the parental strain of the plasmid-free L. lactis IL1403, which is one of the best characterized lactococcal strains of LAB. Complete nucleotide sequences of pIL1 (6,382 bp), pIL2 (8,277 bp), pIL3 (19,244 bp), pIL4 (48,979), pIL5 (23,395), pIL6 (28,435 bp) and pIL7 (28,546) were established and deposited in the generally accessible database (GeneBank). Nine highly homologous repB-containing replicons, belonging to the lactococcal theta-type replicons, have been identified on the seven plasmids. Moreover, a putative region involved in conjugative plasmid mobilization was found on four plasmids, through identification of the presence of mob genes and/or oriT sequences. Detailed bioinformatic analysis of the plasmid nucleotide sequences provided new insight into the repertoire of plasmid-encoded functions in L. lactis, and indicated that plasmid genes from IL594 strain can be important for L. lactis adaptation to specific environmental conditions (e.g. genes coding for proteins involved in DNA repair or cold shock response) as well as for technological processes (e.g. genes encoding citrate and lactose utilization, oligopeptide transport, restriction-modification system). Moreover, global gene analysis indicated cooperation between plasmid- and chromosome-encoded metabolic pathways

Simple methodology for the quantitative analysis of fatty acids in human red blood cells

In the last years, there has been an increasing interest in evaluating possible relations between fatty acid (FA) patterns and the risk for chronic diseases. Due to the long life span (120 days) of red blood cells (RBCs), their FA profile reflects a longer term dietary intake and was recently suggested to be used as an appropriate biomarker to investigate correlations between FA metabolism and diseases. Therefore, the aim of this work was to develop and validate a simple and fast methodology for the quantification of a broad range of FAs in RBCs using gas chromatography with flame ionization detector, as a more common and affordable equipment suitable for biomedical and nutritional studies including a large number of samples. For this purpose, different sample preparation protocols were tested and compared, including a classic two-step method (Folch method) with modifications and different one-step methods, in which lipid extraction and derivatization were performed simultaneously. For the one-step methods, different methylation periods and the inclusion of a saponification reaction were evaluated. Differences in absolute FA concentrations were observed among the tested methods, in particular for some metabolically relevant FAs such as trans elaidic acid and eicosapentaenoic acid. The one-step method with saponification and 60 min of methylation time was selected since it allowed the identification of a higher number of FAs, and was further submitted to in-house validation. The proposed methodology provides a simple, fast and accurate tool to quantitatively analyse FAs in human RBCs, useful for clinical and nutritional studies.This work received financial support from the European Union (FEDER funds through COMPETE) and National Funds (FCT, Fundação para a Ciência e Tecnologia) through project PTDC/SAU-ENB/116929/2010 and EXPL/EMS-SIS/2215/2013. ROR acknowledges PhD scholarship SFRH/BD/97658/2013 attributed by FCT (Fundação para a Ciência e Tecnologia).info:eu-repo/semantics/publishedVersio