Social capital and collusion: the case of merchant guilds

Merchant guilds have been portrayed as ‘social networks’ that generated beneficial ‘social capital’ by sustaining shared norms, effectively transmitting information, and successfully undertaking collective action. This social capital, it is claimed, benefited society as a whole by enabling rulers to commit to providing a secure trading environment for alien merchants. But was this really the case? We develop a new model of the emergence, rise and eventual decline of European merchant guilds which explores the collusive relationship between rulers and guilds, and calls into question the prevailing positive view of merchant guilds. We then confront the model’s predictions with the available historical data. The empirical evidence strongly support our model and refutes existing theories. Our findings show that merchant guilds used their social capital for socially harmful as well as beneficial ends

Identifying the lights position in photometric stereo under unknown lighting

Reconstructing the 3D shape of an object from a set of images is a classical problem in Computer Vision. Photometric stereo is one of the possible approaches. It stands on the assumption that the object is observed from a fixed point of view under different lighting conditions. The traditional approach requires that the position of the light sources is accurately known. It has been proved that the lights position can be estimated directly from the data, when at least 6 images of the observed object are available. In this paper, we give a Matlab implementation of the algorithm for solving the photometric stereo problem under unknown lighting, and propose a simple shooting technique to solve the bas-relief ambiguity.Comment: new versio

Tracing the boundaries of Cenozoic volcanic edifices from Sardinia (Italy): a geomorphometric contribution

Unequivocal delimitation of landforms is an important issue for different purposes, from sciencedriven morphometric analysis to legal issues related to land conservation. This study is aimed at giving a new contribution to the morphometric approach for the delineation of the boundaries of volcanic edifices, applied to 13 monogenetic volcanoes (scoria cones) related to the Pliocene–Pleistocene volcanic cycle in Sardinia (Italy). External boundary delimitation of the edifices is discussed based on an integrated methodology using automatic elaboration of digital elevation models together with geomorphological and geological observations. Different elaborations of surface slope and profile curvature have been proposed and discussed; among them, two algorithms based on simple mathematical functions combining slope and profile curvature well fit the requirements of this study. One of theses algorithms is a modification of a function introduced by Grosse et al. (2011), which better performs for recognizing and tracing the boundary between the volcanic scoria cone and its basement. Although the geological constraints still drive the final decision, the proposed method improves the existing tools for a semi-automatic tracing of the boundaries

ESA SENTINEL 2 IMAGERY AND GBGEOAPP: INTEGRATED TOOLS FOR THE DEOSAI NATIONAL PARK MANAGEMENT PLAN

Deosai plateau, in the Gilgit-Baltistan Province of Pakistan, for its average elevation of 4,114 meters, is the second highest plateau in the world after Changtang Tibetan Plateau. Two biogeographically important mountain ranges merge in Deosai: the Himalayan and Karakorum–Pamir highlands. The Deosai National Park, with its first recognition in 1993, encompasses an area of about 1620 km2, with the altitude ranging from 3500 to 5200 meters a.s.l. It is known and visited by tourists for the presence of brown bear, but a large number of species of fauna and flora leave, and can be seen during the summer season. This high-altitude ecosystem is particularly fragile and can be considered a sentinel for the effects of climate changes. Due to its geographic position and high altitude, the area of Deosai has never been studied in all its ecosystem components, producing high resolution maps. The first land cover map of Deosai with 10 meters of resolution is discussed in this study. This map has been obtained from Sentinel-2 imagery and improved through the new tool developed in this study: the GBGEOApp. This application for mobile has been done with three main ambitions: the validation of the new land cover map, its improvement with land use information, and the collection of new data in the field. On the basis of the results, the use of the GBGEOApp, as a tool for validation and increasing of environmental data collection, seems to be completely applicable involving the local technicians in a process of data sharing

The Science of Stem Cell Biobanking: Investing in the Future

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Sector Neutral Portfolios: Long Memory Motifs Persistence in Market Structure Dynamics

We study soft persistence (existence in subsequent temporal layers of motifs from the initial layer) of motif structures in Triangulated Maximally Filtered Graphs (TMFG) generated from time-varying Kendall correlation matrices computed from stock prices log-returns over rolling windows with exponential smoothing. We observe long-memory processes in these structures in the form of power law decays in the number of persistent motifs. The decays then transition to a plateau regime with a power-law decay with smaller exponent. We demonstrate that identifying persistent motifs allows for forecasting and applications to portfolio diversification. Balanced portfolios are often constructed from the analysis of historic correlations, however not all past correlations are persistently reflected into the future. Sector neutrality has also been a central theme in portfolio diversification and systemic risk. We present an unsupervised technique to identify persistently correlated sets of stocks. These are empirically found to identify sectors driven by strong fundamentals. Applications of these findings are tested in two distinct ways on four different markets, resulting in significant reduction in portfolio volatility. A persistence-based measure for portfolio allocation is proposed and shown to outperform volatility weighting when tested out of sample

RNA analysis of consensus sequence splicing mutations: implications for thediagnosis of Wilson disease

Wilson disease (WD) is an autosomal recessive disorder caused by a defective function of the copper-transporting ATP7B protein. This results in progressive copper overload and consequent liver, brain, and kidney damage. Approximately 300 WD-causing mutations have been described to date. Missense mutations are largely prevalent, while splice-site mutations are rarer. Of these, only a minority are detected in splicing consensus sequences. Further, few splicing mutations have been studied at the RNA level. In this study we report the RNA molecular characterization of three consensus splice-site mutations identified by DNA analysis in WD patients. One of them, c.51 + 4 A --> T, resides in the consensus sequence of the donor splice site of intron 1; the second, c. 2121 + 3 A --> G, occurred in position + 3 of intron 7; and the c.2447 + 5 G --> A is localized in the consensus sequence of the donor splice site of intron 9. Analysis revealed predominantly abnormal splicing in the samples carrying mutations compared to the normal controls. These results strongly suggest that consensus sequence splice-site mutations result in disease by interfering with the production of the normal WD protein. Our data contribute to understanding the mutational spectrum that affect splicing and improve our capability in WD diagnosis

The Association of Left Ventricular Hypertrophy with Metabolic Syndrome is Dependent on Body Mass Index in Hypertensive Overweight or Obese Patients

Overweight (Ow) and obesity (Ob) influence blood pressure (BP) and left ventricular hypertrophy (LVH). It is unclear whether the presence of metabolic syndrome (MetS) independently affects echocardiographic parameters in hypertension.380 Ow/Ob essential hypertensive patients (age ≤ 65 years) presenting for referred BP control-related problems. MetS was defined according to NCEP III/ATP with AHA modifications and LVH as LVM/h(2.7) ≥ 49.2 g/m(2.7) in males and ≥ 46.7 g/m(2.7) in females. Treatment intensity score (TIS) was used to control for BP treatment as previously reported.Hypertensive patients with MetS had significantly higher BMI, systolic and mean BP, interventricular septum and relative wall thickness and lower ejection fraction than those without MetS. LVM/h(2.7) was significantly higher in MetS patients (59.14 ± 14.97 vs. 55.33 ± 14.69 g/m(2.7); p = 0.022). Hypertensive patients with MetS had a 2.3-fold higher risk to have LVH/h(2.7) after adjustment for age, SBP and TIS (OR 2.34; 95%CI 1.40-3.92; p = 0.001), but MetS lost its independent relationship with LVH when BMI was included in the model.In Ow/Ob hypertensive patients MetS maintains its role of risk factor for LVH independently of age, SBP, and TIS, resulting in a useful predictor of target organ damage in clinical practice. However, MetS loses its independent relationship when BMI is taken into account, suggesting that the effects on MetS on LV parameters are mainly driven by the degree of adiposity

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

Delayed diagnosis of coeliac disease increases cancer risk

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