Bubbles and Experience: An Experiment on Speculation

We investigate experimentally how the share of experienced traders in double-auction asset markets affects trading, in particular the occurrence of bubble-crash pricing patterns. In each session, six subjects trade in three successive market rounds and gain experience. In a fourth round, depending on the treatment, two or four experienced subjects are replaced by inexperienced subjects. The results are compared to earlier findings when all traders were either inexperienced or experienced. We explore what can be learned by analogy between these laboratory findings and the performance of naturally occurring markets.Asset Market; Bubble; Crash; Experience; Experiment; Speculation

IL-17 drives copper uptake and activation of growth pathways in colorectal cancer cells in a Steap4-dependent manner

Colorectal cancer is a disease characterized by abnormal, invasive cell growth beginning in the colon or rectum. The third most common type of cancer worldwide, approximately one million new cases of the disease are diagnosed across the globe annually, resulting in an estimated 700,000+ deaths. One major risk factor associated with development of colorectal cancer is the presence of chronic inflammation in the large intestine, also known as colitis. Inflammation is a complex immune response against harmful stimuli, characterized by symptoms including heat, redness, swelling and pain. One important molecular mediator of this process is interleukin 17 (IL-17), a pro-inflammatory cytokine. While acute inflammation is a useful defensive response against invading pathogens, the presence of chronic inflammation is associated with an increased risk of tumorigenesis. Colorectal cancer is frequently observed to metastasize from the colon to the liver, the body’s largest storage site of copper, after which it becomes significantly more difficult to treat effectively. Copper is a trace nutrient required by all living systems, due to its ability to participate in one-electron exchange reactions, a vital mechanism of ubiquitous biological processes. STEAP4, a cell membrane protein, is a copper reductase. In this thesis, data are presented that show that colon cancer cells in which STEAP4 is overexpressed take up more copper from their environment than colon cancer cells in which STEAP4 is expressed normally. Additional data show that IL-17 stimulation, previously linked to colorectal cancer progression, increases copper uptake by colon cancer cells. A mouse model experiment also shows that induction of colitis mobilizes copper from the liver into systemic circulation. Further, it is shown that overexpression of STEAP4 enhances activation of IL-17-mediated growth pathways that have previously been shown to drive cancer progression. Finally, it is shown that colitis-associated colorectal cancer mice treated with a copper chelator may develop fewer tumor nodules that untreated mice. Taken together, these data suggest that IL-17 signaling drives tumor progression through a STEAP4-dependent mechanism of copper uptake. It is further suggested that lowering body copper levels through chelation therapy could be an effective method of stopping colorectal cancer progression

IL-17 Drives Copper Uptake and Activation of Growth Pathways in Colorectal Cancer Cells in a Steap4-dependent Manner

Colorectal cancer is a disease characterized by abnormal, invasive cell growth beginning in the colon or rectum. The third most common type of cancer worldwide, approximately one million new cases of the disease are diagnosed across the globe annually, resulting in an estimated 700,000+ deaths. One major risk factor associated with development of colorectal cancer is the presence of chronic inflammation in the large intestine, also known as colitis. Inflammation is a complex immune response against harmful stimuli, characterized by symptoms including heat, redness, swelling and pain. One important molecular mediator of this process is interleukin 17 (IL-17), a pro-inflammatory cytokine. While acute inflammation is a useful defensive response against invading pathogens, the presence of chronic inflammation is associated with an increased risk of tumorigenesis. Colorectal cancer is frequently observed to metastasize from the colon to the liver, the body’s largest storage site of copper, after which it becomes significantly more difficult to treat effectively. Copper is a trace nutrient required by all living systems, due to its ability to participate in one-electron exchange reactions, a vital mechanism of ubiquitous biological processes. STEAP4, a cell membrane protein, is a copper reductase. In this thesis, data are presented that show that colon cancer cells in which STEAP4 is overexpressed take up more copper from their environment than colon cancer cells in which STEAP4 is expressed normally. Additional data show that IL-17 stimulation, previously linked to colorectal cancer progression, increases copper uptake by colon cancer cells. A mouse model experiment also shows that induction of colitis mobilizes copper from the liver into systemic circulation. Further, it is shown that overexpression of STEAP4 enhances activation of IL-17-mediated growth pathways that have previously been shown to drive cancer progression. Finally, it is shown that colitis-associated colorectal cancer mice treated with a copper chelator may develop fewer tumor nodules that untreated mice. Taken together, these data suggest that IL-17 signaling drives tumor progression through a STEAP4-dependent mechanism of copper uptake. It is further suggested that lowering body copper levels through chelation therapy could be an effective method of stopping colorectal cancer progression

The market reaction to disclosures related to goodwill after SFAS No. 142.

Abstract not available

The Extended Nutrigenomics – Understanding the Interplay between the Genomes of Food, Gut Microbes, and Human Host

Comprehensive investigation of nutritional health effects at the molecular level requires the understanding of the interplay between three genomes, the food, the gut microbial, and the human host genome. Food genomes are researched for discovery and exploitation of macro- and micronutrients as well as specific bioactives, with those genes coding for bioactive proteins and peptides being of central interest. The human gut microbiota encompasses a complex ecosystem in the intestine with profound impact on host metabolism. It is being studied at genomic and, more recently, also at proteomic and metabonomic level. Humans are being characterized at the level of genetic pre-disposition and inter-individual variability in terms of (i) response to nutritional interventions and direction of health trajectories; (ii) epigenetic, metabolic programming at certain life stages with health consequences later in life and even for subsequent generations; and (iii) acute genomic expression as a holistic response to diet, monitored at gene transcript, protein and metabolite level. Modern nutrition science explores health-related aspects of bioactive food components, thereby promoting health, preventing, or delaying the onset of disease, optimizing performance and assessing benefits and risks in individuals and subpopulations. Personalized nutrition means adapting food to individual needs, depending on the human host’s life stage, -style, and -situation. Traditionally, nutrigenomics and nutri(epi)genetics are seen as the key sciences to understand human variability in preferences and requirements for diet as well as responses to nutrition. This article puts the three nutrition and health-relevant genomes into perspective, namely the food, the gut microbial and the human host’s genome, and calls for an “extended nutrigenomics” approach in order to build the future tools for personalized nutrition, health maintenance, and disease prevention. We discuss examples of these genomes, proteomes, transcriptomes, and metabolomes under the definition of genomics as the overarching term covering essentially all Omics rather than the sole study of DNA and RNA

A Bayesian Approach to Directed Acyclic Graphs with a Candidate Graph

Directed acyclic graphs represent the dependence structure among variables. When learning these graphs from data, different amounts of information may be available for different edges. Although many methods have been developed to learn the topology of these graphs, most of them do not provide a measure of uncertainty in the inference. We propose a Bayesian method, baycn (BAYesian Causal Network), to estimate the posterior probability of three states for each edge: present with one direction ($X \rightarrow Y$), present with the opposite direction ($X \leftarrow Y$), and absent. Unlike existing Bayesian methods, our method requires that the prior probabilities of these states be specified, and therefore provides a benchmark for interpreting the posterior probabilities. We develop a fast Metropolis-Hastings Markov chain Monte Carlo algorithm for the inference. Our algorithm takes as input the edges of a candidate graph, which may be the output of another graph inference method and may contain false edges. In simulation studies our method achieves high accuracy with small variation across different scenarios and is comparable or better than existing Bayesian methods. We apply baycn to genomic data to distinguish the direct and indirect targets of genetic variants.Comment: Included analyses for data from GEUVADIS and GTE

The Prehistoric hand pictures at Gargas: attempts at simulation

A number of experimental methods of reconstructing prehistoric hand images like those in the cave of Gargas, France, are described and assessed. The results of experiments using these methods are evaluated from the point of view of the bearing they have on our knowledge about the creation of the original pictures in the cave

The Size and Origin of Metal-enriched Regions in the Intergalactic Medium from Spectra of Binary Quasars

We present tomography of the circum-galactic metal distribution at redshift 1.7-4.5 derived from echellete spectroscopy of binary quasars. We find C IV systems at similar redshifts in paired sightlines more often than expected for sightline-independent redshifts. As the separation of the sightlines increases from 36 kpc to 907 kpc, the amplitude of this clustering decreases. At the largest separations, the C IV systems cluster similar to the Lyman-break galaxies studied by Adelberger et al. in 2005. The C IV systems are significantly less correlated than these galaxies, however, at separations less than R_1 0.42 ± 0.15 h^( –1) comoving Mpc. Measured in real space, i.e., transverse to the sightlines, this length scale is significantly smaller than the break scale estimated previously from the line-of-sight correlation function in redshift space by Scannapieco et al. in 2006. Using a simple model, we interpret the new real-space measurement as an indication of the typical physical size of enriched regions. We adopt this size for enriched regions and fit the redshift-space distortion in the line-of-sight correlation function. The fitted velocity kick is consistent with the peculiar velocity of galaxies as determined by the underlying mass distribution and places an upper limit on the average outflow (or inflow) speed of metals. The implied timescale for dispersing metals is larger than the typical stellar ages of Lyman-break galaxies, and we argue that enrichment by galaxies at z > 4.3 played a greater role in dispersing metals. To further constrain the growth of enriched regions, we discuss empirical constraints on the evolution of the C IV correlation function with cosmic time. This study demonstrates the potential of tomography for measuring the metal enrichment history of the circum-galactic medium