Financing constraints, irreversibility and investment dynamics

We develop a model of an industry with many heterogeneous firms that face both financingconstraints and irreversibility constraints. The financing constraint implies that firmscannot borrow unless the debt is secured by collateral; the irreversibility constraint thatthey can only sell their fixed capital by selling their business. We use this model to examinethe cyclical behavior of aggregate fixed investment, variable capital investment, and outputin the presence of persistent idiosyncratic and aggregate shocks. Our model yields threemain results. First, the effect of the irreversibility constraint on fixed capital investmentis reinforced by the financing constraint. Second, the effect of the financing constraint onvariable capital investment is reinforced by the irreversibility constraint. Finally, the interactionbetween the two constraints is key for explaining why input inventories and materialdeliveries of US manufacturing firms are so volatile and procyclical, and also why they arehighly asymmetrical over the business cycle.

Beta cell mass and growth after syngeneic islet cell transplantation in normal and streptozocin diabetic C57BL/6 mice

In islet transplantation, nonimmunological factors such as limited growth capacity or increased death rate could reduce the beta cell mass in the graft and lead to failure of the transplant. We studied the evolution of beta cell replication and mass after transplantation of insufficient, minimally sufficient, or excessive islet tissue. Streptozocin diabetic C57BL/6 mice received 150 or 300 syngeneic islets under the kidney capsule and normal mice received 300 islets. In streptozocin diabetic mice 300 islets restored normoglycemia; beta cell replication in transplanted islets was similar to replication in normal pancreas and beta cell mass in the graft remained constant. In contrast, 150 islets were insufficient to achieve normoglycemia; beta cell replication was increased initially but not by 18 or 30 d despite persistent hyperglycemia, and beta cell mass fell progressively. When islets were transplanted into normal recipients, beta cell replication remained normal but beta cells underwent atrophy and mass in the graft was substantially reduced. Therefore, with a successful islet transplant, in diabetic mice beta cell replication and mass remain constant. In contrast, when insufficient islet tissue is transplanted an initial increase in beta cell replication can not compensate for a decline in beta cell mass. When excessive islet tissue is transplanted, beta cell mass is reduced despite normal beta cell replication.

MAR elements regulate the probability of epigenetic switching between active and inactive gene expression.

Gene expression often cycles between active and inactive states in eukaryotes, yielding variable or noisy gene expression in the short-term, while slow epigenetic changes may lead to silencing or variegated expression. Understanding how cells control these effects will be of paramount importance to construct biological systems with predictable behaviours. Here we find that a human matrix attachment region (MAR) genetic element controls the stability and heritability of gene expression in cell populations. Mathematical modeling indicated that the MAR controls the probability of long-term transitions between active and inactive expression, thus reducing silencing effects and increasing the reactivation of silent genes. Single-cell short-terms assays revealed persistent expression and reduced expression noise in MAR-driven genes, while stochastic burst of expression occurred without this genetic element. The MAR thus confers a more deterministic behavior to an otherwise stochastic process, providing a means towards more reliable expression of engineered genetic systems.

Comparison between detailed digital and conventional soil maps of an area with complex geology

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

Association of ECG abnormalities and incident heart failure events

Background: With the aging of the population, the heart failure (HF) incidence and prevalence trends are expected to significantly worsen unless concentrated prevention efforts are undertaken. ECG abnormalities are common in the elderly but data are limited for their association with HF risk. Objective: To assess whether baseline ECG abnormalities or dynamic changes are associated with an increased risk of HF. Method: A prospective cohort study of 2915 participants aged 70 to 79 years without a preexisting HF followed for a median period of 11.4 (IQR 7.0-11.7) years from the Health Aging and Body Composition study. The Minnesota Code was used to define major and minor ECG abnormalities at baseline and at 4-year. Main outcome measure was adjudicated incident HF events. Using Cox models, the (1) the association between ECG abnormalities and incident HF and (2) incremental value of adding ECG to the Health ABC HF Risk Score, was assessed. Results: At baseline, 380 participants (13.0%) had minor and 620 (21.3%) had major ECG abnormalities. During follow-up, 485 (16.6%) participants developed incident HF. After adjusting for the eight clinical variables in the Health ABC HF Risk Score, the hazard ratio (HR) was 1.27 (95% confidence interval [CI] 0.96-1.68) for minor and 1.99 (CI 1.61-2.44) for major ECG abnormalities (P for trend <0.001) compared to no ECG abnormalities. The association did not change according to presence of baseline CHD. At 4-year, 263 participants developed new and 549 had persistent abnormalities and both were associated with increased HF risk (HR = 1.94, CI 1.38-2.72 for new and HR=2.35, CI 1.82-3.02 for persistent compared to no ECG abnormalities). Baseline ECG correctly reclassified 10.6% of overall participants across the categories of the Health ABC HF Risk Score. Conclusion: Among older adults, baseline ECG abnormalities and changes in them over time are common; both are associated with an increased risk of HF. Whether ECG should be incorporated in routine screening of older adults should be evaluated in randomized controlled trials.

Assessing the associations between mental disorders, cardiovascular risk factors, and cardiovascular disease : the CoLaus/PsyCoLaus study

Background: Cardio-vascular diseases (CVD), their well established risk factors (CVRF) and mental disorders are common and co-occur more frequently than would be expected by chance. However, the pathogenic mechanisms and course determinants of both CVD and mental disorders have only been partially identified.Methods/Design: Comprehensive follow-up of CVRF and CVD with a psychiatric exam in all subjects who participated in the baseline cross-sectional CoLaus study (2003-2006) (n=6'738) which also included a comprehensive genetic assessment. The somatic investigation will include a shortened questionnaire on CVRF, CV events and new CVD since baseline and measurements of the same clinical and biological variables as at baseline. In addition, pro-inflammatory markers, persistent pain and sleep patterns and disorders will be assessed. In the case of a new CV event, detailed information will be abstracted from medical records. Similarly, data on the cause of death will be collected from the Swiss National Death Registry. The comprehensive psychiatric investigation of the CoLaus/PsyCoLaus study will use contemporary epidemiological methods including semi-structured diagnostic interviews, experienced clinical interviewers, standardized diagnostic criteria including threshold according to DSM-IV and sub-threshold syndromes and supplementary information on risk and protective factors for disorders. In addition, screening for objective cognitive impairment will be performed in participants older than 65 years.Discussion: The combined CoLaus/PsyCoLaus sample provides a unique opportunity to obtain prospective data on the interplay between CVRF/CVD and mental disorders, overcoming limitations of previous research by bringing together a comprehensive investigation of both CVRF and mental disorders as well as a large number of biological variables and a genome-wide genetic assessment in participants recruited from the general population.

Prevalence and impact of cannabis use disorders in adolescents with early onset first episode psychosis.

BACKGROUND: Previous studies on the impact of cannabis use disorders (CU) on outcome in psychosis were predominantly based on non representative samples, often have not controlled for confounders and rarely focused on adolescent patients. Thus, the aims of the present study were to assess: (i) prevalence of CU; (ii) baseline and pretreatment differences between CU and those without CU (NCU); (iii) the impact of baseline and course of CU on 18-month outcomes in a representative cohort of adolescents with early onset first episode psychosis (EOP). METHODS: The sample comprised 99 adolescents (age 14 to 18) with EOP (onset age 14 to 17), admitted to the Early Psychosis Prevention and Intervention Centre in Australia. Data were collected from medical files using a standardized questionnaire. RESULTS: Prevalence of lifetime CU was 65.7%, of current CU at baseline 53.5%, and of persistent CU throughout treatment 26.3%. Baseline CU compared to NCU had significantly higher illness-severity, lower psychosocial functioning, less insight, lower premorbid functioning and longer duration of untreated psychosis. Compared to all other groups, only persistent CU was linked to worse outcomes and more service disengagement. Effect sizes were medium controlling for relevant confounders. Medication non-adherence did not explain the association between persistent CU and worse outcome. CONCLUSIONS: Baseline CU was associated with worse baseline characteristics, but only persistent CU was linked with worse outcome. About half of those with baseline CU reduced cannabis during treatment. For these, effectively treating the psychotic disorder may already be beneficial. However, future research is necessary on the reasons for persistent CU in EOP and its treatment.

A new locus for congenital cataract, microcornea, microphthalmia, and atypical iris coloboma maps to chromosome 2.

OBJECTIVE: To report a novel phenotype of autosomal dominant atypical congenital cataract associated with variable expression of microcornea, microphthalmia, and iris coloboma linked to chromosome 2. Molecular analysis of this phenotype may improve our understanding of anterior segment development. DESIGN: Observational case study, genome linkage analysis, and gene mutation screening. PARTICIPANTS: Three families, 1 Egyptian and 2 Belgians, with a total of 31 affected were studied. METHODS: Twenty-one affected subjects and 9 first-degree relatives underwent complete ophthalmic examination. In the Egyptian family, exclusion of PAX6, CRYAA, and MAF genes was demonstrated by haplotype analysis using microsatellite markers on chromosomes 11, 16, and 21. Genome-wide linkage analysis was then performed using 385 microsatellite markers on this family. In the 2 Belgian families, the PAX6 gene was screened for mutations by direct sequencing of all exons. MAIN OUTCOME MEASURES: Phenotype description, genome-wide linkage of the phenotype, linkage to the PAX6, CRYAA, and MAF genes, and mutation detection in the PAX6 gene. RESULTS: Affected members of the 3 families had bilateral congenital cataracts inherited in an autosomal dominant pattern. A novel form of hexagonal nuclear cataract with cortical riders was expressed. Among affected subjects with available data, 95% had microcornea, 39% had microphthalmia, and 38% had iris coloboma. Seventy-five percent of the colobomata were atypical, showing a nasal superior location in 56%. A positive lod score of 4.86 was obtained at theta = 0 for D2S2309 on chromosome 2, a 4.9-Mb common haplotype flanked by D2S2309 and D2S2358 was obtained in the Egyptian family, and linkage to the PAX6, CRYAA, or MAF gene was excluded. In the 2 Belgian families, sequencing of the junctions and all coding exons of PAX6 did not reveal any molecular change. CONCLUSIONS: We describe a novel phenotype that includes the combination of a novel form of congenital hexagonal cataract, with variably expressed microcornea, microphthalmia, and atypical iris coloboma, not caused by PAX6 and mapping to chromosome 2. FINANCIAL DISCLOSURE(S): The authors have no proprietary or commercial interest in any materials discussed in this article.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Copy number variants, diseases and gene expression.

Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation likely to play a role in phenotypic diversity and evolution. Much effort has been put into the identification and mapping of regions that vary in copy number among seemingly normal individuals in humans and a number of model organisms, using bioinformatics or hybridization-based methods. These have allowed uncovering associations between copy number changes and complex diseases in whole-genome association studies, as well as identify new genomic disorders. At the genome-wide scale, however, the functional impact of CNV remains poorly studied. Here we review the current catalogs of CNVs, their association with diseases and how they link genotype and phenotype. We describe initial evidence which revealed that genes in CNV regions are expressed at lower and more variable levels than genes mapping elsewhere, and also that CNV not only affects the expression of genes varying in copy number, but also have a global influence on the transcriptome. Further studies are warranted for complete cataloguing and fine mapping of CNVs, as well as to elucidate the different mechanisms by which they influence gene expression.

Textual autocorrelation : formalism and illustrations

Abstract Textual autocorrelation is a broad and pervasive concept, referring to the similarity between nearby textual units: lexical repetitions along consecutive sentences, semantic association between neighbouring lexemes, persistence of discourse types (narrative, descriptive, dialogal...) and so on. Textual autocorrelation can also be negative, as illustrated by alternating phonological or morpho-syntactic categories, or the succession of word lengths. This contribution proposes a general Markov formalism for textual navigation, and inspired by spatial statistics. The formalism can express well-known constructs in textual data analysis, such as term-document matrices, references and hyperlinks navigation, (web) information retrieval, and in particular textual autocorrelation, as measured by Moran's I relatively to the exchange matrix associated to neighbourhoods of various possible types. Four case studies (word lengths alternation, lexical repulsion, parts of speech autocorrelation, and semantic autocorrelation) illustrate the theory. In particular, one observes a short-range repulsion between nouns together with a short-range attraction between verbs, both at the lexical and semantic levels. Résumé: Le concept d'autocorrélation textuelle, fort vaste, réfère à la similarité entre unités textuelles voisines: répétitions lexicales entre phrases successives, association sémantique entre lexèmes voisins, persistance du type de discours (narratif, descriptif, dialogal...) et ainsi de suite. L'autocorrélation textuelle peut être également négative, comme l'illustrent l'alternance entre les catégories phonologiques ou morpho-syntaxiques, ou la succession des longueurs de mots. Cette contribution propose un formalisme markovien général pour la navigation textuelle, inspiré par la statistique spatiale. Le formalisme est capable d'exprimer des constructions bien connues en analyse des données textuelles, telles que les matrices termes-documents, les références et la navigation par hyperliens, la recherche documentaire sur internet, et, en particulier, l'autocorélation textuelle, telle que mesurée par le I de Moran relatif à une matrice d'échange associée à des voisinages de différents types possibles. Quatre cas d'étude illustrent la théorie: alternance des longueurs de mots, répulsion lexicale, autocorrélation des catégories morpho-syntaxiques et autocorrélation sémantique. On observe en particulier une répulsion à courte portée entre les noms, ainsi qu'une attraction à courte portée entre les verbes, tant au niveau lexical que sémantique.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

Persistent left superior vena cava in cardiac congenital surgery.

Persistent left superior vena cava (LSVC) is a relatively frequent finding in congenital cardiac malformation. The scope of the study was to analyze the timing of diagnosis of persistent LSVC, the timing of diagnosis of associated anomalies of the coronary sinus, and the global impact on morbidity and mortality of persistent LSVC in children with congenital heart disease after cardiac surgery. Retrospective analysis of a cohort of children after cardiac surgery on bypass for congenital heart disease. Three hundred seventy-one patients were included in the study, and their median age was 2.75 years (IQR 0.65-6.63). Forty-seven children had persistent LSVC (12.7 %), and persistent LSVC was identified on echocardiography before surgery in 39 patients (83 %). In three patients (6.4 %) with persistent LSVC, significant inflow obstruction of the left ventricle developed after surgery leading to low output syndrome or secondary pulmonary hypertension. In eight patients (17 %), persistent LSVC was associated with a partially or completely unroofed coronary sinus and in two cases (4 %) with coronary sinus ostial atresia. Duration of mechanical ventilation was significantly shorter in the control group (1.2 vs. 3.0 days, p = 0.04), whereas length of stay in intensive care did not differ. Mortality was also significantly lower in the control group (2.5 vs. 10.6 %, p = 0.004). The results of study show that persistent LSVC in association with congenital cardiac malformation increases the risk of mortality in children with cardiac surgery on cardiopulmonary bypass. Recognition of a persistent LSVC and its associated anomalies is mandatory to avoid complications during or after cardiac surgery.

Infection and chronic allograft dysfunction.

With the advent of more potent immunosuppressive regimens, the incidence of acute rejection following renal transplantation has declined sharply in recent years. In spite of this, long-term graft outcomes remain suboptimal because of relentless attrition by cumulated insults to the allograft. As acute rejection rates have declined, other causes of graft injury and loss have recently emerged. Among these, infectious diseases remain a persistent threat and can be associated with allograft dysfunction. This group includes nephropathy due to polyoma (BK) virus infection, cytomegalovirus disease, and bacterial infection (the latter most commonly arising from the urinary tract). Rarer infectious causes of chronic allograft dysfunction include cryoglobulinemia associated with hepatitis C, Epstein-Barr virus-associated posttransplant lymphoproliferative disease, and direct cytotoxicity from adenoviral infection or parvovirus B19.