Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.

Modeling resting-state functional networks when the cortex falls asleep: local and global changes.

The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network.

Congenital heart disease in pregnancies complicated by maternal diabetes mellitus. An international clinical collaboration, literature review, and meta-analysis.

PURPOSE: Investigation of the incidence and distribution of congenital structural cardiac malformations among the offspring of mothers with diabetes type 1 and of the influence of periconceptional glycemic control. METHODS: Multicenter retrospective clinical study, literature review, and meta-analysis. The incidence and pattern of congenital heart disease in the own study population and in the literature on the offspring of type 1 diabetic mothers were compared with the incidence and spectrum of the various cardiovascular defects in the offspring of nondiabetic mothers as registered by EUROCAT Northern Netherlands. Medical records were, in addition, reviewed for HbA(1c) during the 1st trimester. RESULTS: The distribution of congenital heart anomalies in the own diabetic study population was in accordance with the distribution encountered in the literature. This distribution differed considerably from that in the nondiabetic population. Approximately half the cardiovascular defects were conotruncal anomalies. The authors' study demonstrated a remarkable increase in the likelihood of visceral heterotaxia and variants of single ventricle among these patients. As expected, elevated HbA(1c) values during the 1st trimester were associated with offspring fetal cardiovascular defects. CONCLUSION: This study shows an increased likelihood of specific heart anomalies, namely transposition of the great arteries, persistent truncus arteriosus, visceral heterotaxia and single ventricle, among offspring of diabetic mothers. This suggests a profound teratogenic effect at a very early stage in cardiogenesis. The study emphasizes the frequency with which the offspring of diabetes-complicated pregnancies suffer from complex forms of congenital heart disease. Pregnancies with poor 1st-trimester glycemic control are more prone to the presence of fetal heart disease.

Cohort profile: Antiretroviral Therapy Cohort Collaboration (ART-CC).

The advent of effective combination antiretroviral therapy (ART) in 1996 resulted in fewer patients experiencing clinical events, so that some prognostic analyses of individual cohort studies of human immunodeficiency virus-infected individuals had low statistical power. Because of this, the Antiretroviral Therapy Cohort Collaboration (ART-CC) of HIV cohort studies in Europe and North America was established in 2000, with the aim of studying the prognosis for clinical events in acquired immune deficiency syndrome (AIDS) and the mortality of adult patients treated for HIV-1 infection. In 2002, the ART-CC collected data on more than 12,000 patients in 13 cohorts who had begun combination ART between 1995 and 2001. Subsequent updates took place in 2004, 2006, 2008, and 2010. The ART-CC data base now includes data on more than 70,000 patients participating in 19 cohorts who began treatment before the end of 2009. Data are collected on patient demographics (e.g. sex, age, assumed transmission group, race/ethnicity, geographical origin), HIV biomarkers (e.g. CD4 cell count, plasma viral load of HIV-1), ART regimen, dates and types of AIDS events, and dates and causes of death. In recent years, additional data on co-infections such as hepatitis C; risk factors such as smoking, alcohol and drug use; non-HIV biomarkers such as haemoglobin and liver enzymes; and adherence to ART have been collected whenever available. The data remain the property of the contributing cohorts, whose representatives manage the ART-CC via the steering committee of the Collaboration. External collaboration is welcomed. Details of contacts are given on the ART-CC website (www.art-cohort-collaboration.org).

HIV-1 drug resistance transmission networks in southwest Switzerland.

To determine viral subtypes and resistance mutations to antiretroviral treatment (ART) in untreated HIV-1 acutely infected subjects from Southwest Switzerland. Clinical samples were obtained from the HIV primary infection cohort from Lausanne. Briefly, pol gene was amplified by nested PCR and sequenced to generate a 1?kb sequence spanning protease and reverse transcriptase key protein regions. Nucleotide sequences were used to assess viral genotype and ART resistance mutations. Blood specimens and medical information were obtained from 30 patients. Main viral subtypes corresponded to clade B, CRF02_AG, and F1. Resistant mutations to PIs consisted of L10V and accessory mutations 16E and 60E present in all F1 clades. The NNRTI major resistant mutation 103N was detected in all F1 viruses and in other 2 clades. Additionally, we identified F1 sequences from other 6 HIV infected and untreated individuals from Southwest Switzerland, harboring nucleotide motifs and resistance mutations to ART as observed in the F1 strains from the cohort. These data reveal a high transmission rate (16.6%) for NNRTI resistant mutation 103N in a cohort of HIV acute infection. Three of the 5 resistant strains were F1 clades closely related to other F1 isolates from HIV-1 infection untreated patients also coming from Southwest Switzerland. Overall, we provide strong evidence towards an HIV-1 resistant transmission network in Southwest Switzerland. These findings have relevant implications for the local molecular mapping of HIV-1 and future ART surveillance studies in the region.

Understanding Interactions in Social Networks and Committees

While much of the literature on cross section dependence has focused mainly on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spill-overs and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful interpretation and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Our methods are applied to a study of committee decision making within the Bank of England’s monetary policy committee.

Evidence for transcript networks composed of chimeric RNAs in human cells.

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

The Impact of Information Provision on Agglomeration Bonus Performance: An Experimental Study on Local Networks

The Agglomeration Bonus (AB) is a mechanism to induce adjacent landowners to spatially coordinate their land use for the delivery of ecosystem services from farmland. This paper uses laboratory experiments to explore the performance of the AB in achieving the socially optimal land management configuration in a local network environment where the information available to subjects varies. The AB poses a coordination problem between two Nash equilibria: a Pareto dominant and a risk dominant equilibrium. The experiments indicate that if subjects are informed about both their direct and indirect neighbors’ actions, they are more likely to coordinate on the Pareto dominant equilibrium relative to the case where subjects have information about their direct neighbors’ action only. However, the extra information can only delay – and not prevent – the transition to the socially inferior risk dominant Nash equilibrium. In the long run, the AB mechanism may only be partially effective in enhancing delivery of ecosystem services on farming landscapes featuring local networks.

How collaboration could contribute to make Catalan journals visible on the Internet?

Cooperation between libraries is a universal language spoken in different dialects. In 1996 the libraries of the state-funded universities and the National Library of Catalonia (Spain) formed the Consortium of Academic Libraries of Catalonia (CBUC) to act as a channel for cooperation. The organization and activities of CBUC are an example of how this universal language has been adapted to the specific characteristics of the Libraries of Catalonia. Catalonia is an autonomous region of Spain with 7 million inhabitants with its own language, history and traditions and with a strong feeling of own identity that facilitates the cooperation. Thanks to this (and also to the hard work of the member libraries), since then, CBUC has created a union catalogue, an interlibrary lending program, the Digital Library of Catalonia, a cooperative store, different cooperatives repositories and other cooperation programs. One of these cooperatives repositories is RACO (Catalan Journals in Open Access, www.raco.cat) where can be consulted, in open access, the full-text articles of scientific, cultural and scholar Catalan journals. The main purpose of RACO is to increase the visibility and searches of the journals included and to spread the scientific and academic production published in Catalonia. This purpose makes specific in three aims: encourage the electronic edition of Catalan journals; be the interface that allows the whole search of all the journals and provide the instruments for its preservation. There are currently 244 journals in RACO, that includes more than 85.000 articles (80% in OA) from 50 publishing institutions. Since it got into operation it has had more than 4 millions of queries. These 244 journals offer the full-text of all the published issues. Nevertheless, some journal can have a delay between the introduction of the table of contents and the full-text for the recent issues. From 2005 we have a plan of retrospective digitization that has allowed to digitize more than 350.000 pages of back issues. The RACO repository works with the open source program OJS (Open Journal Systems, http://pkp.sfu.ca/ojs/) and uses Dublin Core Metadata and the interoperability protocol created by Open Archives Initiative (OAI) which allows to increase the visibility of the articles published in journals offering oneself together with other international repositories.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.