Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells.

Pluripotency in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) is regulated by three transcription factors-OCT3/4, SOX2, and NANOG. To fully exploit the therapeutic potential of these cells it is essential to have a good mechanistic understanding of the maintenance of self-renewal and pluripotency. In this study, we demonstrate a powerful systems biology approach in which we first expand literature-based network encompassing the core regulators of pluripotency by assessing the behavior of genes targeted by perturbation experiments. We focused our attention on highly regulated genes encoding cell surface and secreted proteins as these can be more easily manipulated by the use of inhibitors or recombinant proteins. Qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify novel pluripotency-regulating genes. We validated Interleukin-11 (IL-11) and demonstrate that this cytokine is a novel pluripotency-associated factor capable of supporting self-renewal in the absence of exogenously added bFGF in culture. To date, the various protocols for hESCs maintenance require supplementation with bFGF to activate the Activin/Nodal branch of the TGFβ signaling pathway. Additional evidence supporting our findings is that IL-11 belongs to the same protein family as LIF, which is known to be necessary for maintaining pluripotency in mouse but not in human ESCs. These cytokines operate through the same gp130 receptor which interacts with Janus kinases. Our finding might explain why mESCs are in a more naïve cell state compared to hESCs and how to convert primed hESCs back to the naïve state. Taken together, our integrative modeling approach has identified novel genes as putative candidates to be incorporated into the expansion of the current gene regulatory network responsible for inducing and maintaining pluripotency.

Self Instructional Math, HR-324, 1995

The Self Instructional Math course book is designed to provide a basic math knowledge for those involved in the planning, design, and construction of highways. It was developed in a manner to allow the student to take the course with minimal supervision and at times that the work schedule allows. The first version of the course was developed in the early 1970's and due to its popularity was revised in the early 1990's to reflect changes in the highway construction math needs. The anticipated move to metric (System International) measurements by the highway industry has necessitated the need to change the math course problem values to metric units. The course includes the latest in Iowa DOT policy information relative to the selection and use of metric values for highway design, and construction. Each unit of the book contains instructional information, section quizzes and a comprehensive examination. All problem values are expressed in metric rather than dual (english and SI) units. The appendix contains useful conversion factors to assist the reader in making the change to metric.

Besoins, prescriptions et sécurité des produits sanguins labiles ; autosuffisance en produits sanguins labiles [Roundtables of SFTS Congress 2013: Needs, indications and safety of blood products; self-sufficiency in blood products].

The current issues debate brings together experts around the themes of self-sufficiency (in its national and European aspects) and of needs in cellular blood products. The point of view of the manufacturer and prescribers of blood products are confronted.

Insights into the Design and Characteristics of the Sedimentation Process at Multi-Barrel Culverts in Iowa, TR-596, 2010

The present study is an integral part of a broader study focused on the design and implementation of self-cleaning culverts, i.e., configurations that prevent the formation of sediment deposits after culvert construction or cleaning. Sediment deposition at culverts is influenced by many factors, including the size and characteristics of material of which the channel is composed, the hydraulic characteristics generated under different hydrology events, the culvert geometry design, channel transition design, and the vegetation around the channel. The multitude of combinations produced by this set of variables makes the investigation of practical situations a complex undertaking. In addition to the considerations above, the field and analytical observations have revealed flow complexities affecting the flow and sediment transport through culverts that further increase the dimensions of the investigation. The flow complexities investigated in this study entail: flow non-uniformity in the areas of transition to and from the culvert, flow unsteadiness due to the flood wave propagation through the channel, and the asynchronous correlation between the flow and sediment hydrographs resulting from storm events. To date, the literature contains no systematic studies on sediment transport through multi-box culverts or investigations on the adverse effects of sediment deposition at culverts. Moreover, there is limited knowledge about the non-uniform, unsteady sediment transport in channels of variable geometry. Furthermore, there are few readily useable (inexpensive and practical) numerical models that can reliably simulate flow and sediment transport in such complex situations. Given the current state of knowledge, the main goal of the present study is to investigate the above flow complexities in order to provide the needed insights for a series of ongoing culvert studies. The research was phased so that field observations were conducted first to understand the culvert behavior in Iowa landscape. Modeling through complementary hydraulic model and numerical experiments was subsequently carried out to gain the practical knowledge for the development of the self-cleaning culvert designs.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Redes sociales online, género y construcción del self

Las Redes Sociales Online (RSO) son cada día más populares. Las últimas investigaciones destacan su relevancia en el proceso de construcción de la identidad de género, ya que se encuentran los estereotipos de género clásicos y modulan el bienestar psicológico de los usuarios. Debido a la carencia de estudios españoles, el objetivo de la presente investigación fue conocer los perfiles de los jóvenes en la red social Facebook en términos de estereotipos de género, de personalidad y para comprender qué relación tiene una determinada presentación con el bienestar psicológico. Participaron en el estudio 112 jóvenes con una media de 23 años. Los instrumentos utilizados fueron la adaptación española del test TIPI (Oberst, Renau, Gosling & Rusiñol, manuscrito no publicado), la escala Redsocs y la Escala de Bienestar Psicológico de Ryff (Díaz & et al., 2006). Los resultados indicaron que las chicas estaban más implicadas en las RSO que los chicos, y que en ellas quieren generar un perfil andrógino, ya que para su bienestar psicológico valoran positivamente poseer tanto características femeninas como masculinas. Se concluye que la lucha para combatir los estereotipos de género se libra en un entorno offline o face to face, ya que las RSO ayudan a controlar estas diferencias.

Viral self-assembly as a thermodynamic process

The protein shells, or capsids, of nearly all spherelike viruses adopt icosahedral symmetry. In the present Letter, we propose a statistical thermodynamic model for viral self-assembly. We find that icosahedral symmetry is not expected for viral capsids constructed from structurally identical protein subunits and that this symmetry requires (at least) two internal switching configurations of the protein. Our results indicate that icosahedral symmetry is not a generic consequence of free energy minimization but requires optimization of internal structural parameters of the capsid proteins

Subjective mental time: the functional architecture of projecting the self to past and future.

Abstract Human experience takes place in the line of mental time (MT) created through 'self-projection' of oneself to different time-points in the past or future. Here we manipulated self-projection in MT not only with respect to one's life events but also with respect to one's faces from different past and future time-points. Behavioural and event-related functional magnetic resonance imaging activity showed three independent effects characterized by (i) similarity between past recollection and future imagination, (ii) facilitation of judgements related to the future as compared with the past, and (iii) facilitation of judgements related to time-points distant from the present. These effects were found with respect to faces and events, and also suggest that brain mechanisms of MT are independent of whether actual life episodes have to be re-experienced or pre-experienced, recruiting a common cerebral network including the anteromedial temporal, posterior parietal, inferior frontal, temporo-parietal and insular cortices. These behavioural and neural data suggest that self-projection in time is a fundamental aspect of MT, relying on neural structures encoding memory, mental imagery and self.

Self-Consolidating Concrete—Applications for Slip-Form Paving: Phase I (Feasibility Study), 2005

Over-consolidation is often visible as longitudinal vibrator trails in the surface of concrete pavements constructed using slip-form paving. Concrete research and practice have shown that concrete material selection and mix design can be tailored to provide a good compaction without the need for vibration. However, a challenge in developing self-consolidating concrete for slip-form paving (SF SCC) is that the new SF SCC needs to possess not only excellent self-compactibility and stability before extrusion, but also sufficient “green” strength after extrusion, while the concrete is still in a plastic state. The SF SCC to be developed will not be as fluid as the conventional SCC, but it will (1) be workable enough for machine placement, (2) be self-compacting with minimum segregation, (3) hold shape after extrusion from a paver, and (4) have performance properties (strength and durability) compatible to current pavement concrete. The overall objective of this project is to develop a new type of SCC for slip-form paving to produce more workable concrete and smoother pavements, better consolidation of the plastic concrete, and higher rates of production. Phase I demonstrated the feasibility of designing a new type of SF SCC that can not only self-consolidate, but also have sufficient green strength. In this phase, a good balance between flowability and shape stability was achieved by adopting and modifying the mix design of self-consolidating concrete to provide a high content of fine materials in the fresh concrete. It was shown that both the addition of fine particles and the modification of the type of plasticizer significantly improve fresh concrete flowability. The mixes used in this phase were also found to have very good shape stability in the fresh state. Phase II will focus on developing a SF SCC mix design in the lab and a performing a trial of the SF SCC in the field. Phase III will include field study, performance monitoring, and technology transfer.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

Malignant gastroduodenal obstruction: palliation with self-expanding metallic stents.

PURPOSE: To evaluate the feasibility, efficacy, and tolerance of self-expanding metallic stent insertion under fluoroscopic guidance for palliation of symptoms related to malignant gastroduodenal obstruction. MATERIALS AND METHODS: Seventy-two patients (38 men, 34 women) aged 25-98 years (mean, 62 years) with duodenal (n = 43), antropyloric (n = 13), surgical gastrojejunostomy (n = 10), or pyloroduodenal (n = 6) malignant obstruction were referred for insertion of self-expanding metallic stents over a 6-year period. Stent insertion was performed with use of a peroral or transgastric approach when necessary (n = 11). RESULTS: Stents were successfully inserted in 70 of the 72 patients (97%) and provided symptom relief in 65 patients (90%). Inserted stents were mainly uncovered vascular (n = 55) or enteral (n = 10) Wallstents. One hundred eight stents were initially inserted: one, two, three, or four stents were indicated in 43, 17, nine, and one patient, respectively. Mean follow-up was 119 days (range, 4-513 days). Mean stent patency was 113 days (range, 4-513 days). Mean survival of patients was 120 days. During follow-up, stent obstruction occurred in seven patients as a result of tumoral overgrowth (n = 5) or ingrowth (n = 2). Complications occurred in 12 of the 72 patients (17%), including stent migration (n = 8), stent fracture (n = 1), duodenal perforation (n = 1), and death related to general anesthesia (n = 1). CONCLUSION: Despite a significant complication rate, self-expanding metallic stent insertion under fluoroscopic guidance appears to be a feasible and useful technique in the palliative management of malignant gastroduodenal obstruction.

Free-breathing whole-heart coronary MRA with 3D radial SSFP and self-navigated image reconstruction.

Respiratory motion is a major source of artifacts in cardiac magnetic resonance imaging (MRI). Free-breathing techniques with pencil-beam navigators efficiently suppress respiratory motion and minimize the need for patient cooperation. However, the correlation between the measured navigator position and the actual position of the heart may be adversely affected by hysteretic effects, navigator position, and temporal delays between the navigators and the image acquisition. In addition, irregular breathing patterns during navigator-gated scanning may result in low scan efficiency and prolonged scan time. The purpose of this study was to develop and implement a self-navigated, free-breathing, whole-heart 3D coronary MRI technique that would overcome these shortcomings and improve the ease-of-use of coronary MRI. A signal synchronous with respiration was extracted directly from the echoes acquired for imaging, and the motion information was used for retrospective, rigid-body, through-plane motion correction. The images obtained from the self-navigated reconstruction were compared with the results from conventional, prospective, pencil-beam navigator tracking. Image quality was improved in phantom studies using self-navigation, while equivalent results were obtained with both techniques in preliminary in vivo studies.

The complementary strand of the human T-cell leukemia virus type 1 RNA genome encodes a bZIP transcription factor that down-regulates viral transcription.

The RNA genome of the human T-cell leukemia virus type 1 (HTLV-1) codes for proteins involved in infectivity, replication, and transformation. We report in this study the characterization of a novel viral protein encoded by the complementary strand of the HTLV-1 RNA genome. This protein, designated HBZ (for HTLV-1 bZIP factor), contains a N-terminal transcriptional activation domain and a leucine zipper motif in its C terminus. We show here that HBZ is able to interact with the bZIP transcription factor CREB-2 (also called ATF-4), known to activate the HTLV-1 transcription by recruiting the viral trans-activator Tax on the Tax-responsive elements (TxREs). However, we demonstrate that the HBZ/CREB-2 heterodimers are no more able to bind to the TxRE and cyclic AMP response element sites. Taking these findings together, the functional inactivation of CREB-2 by HBZ is suggested to contribute to regulation of the HTLV-1 transcription. Moreover, the characterization of a minus-strand gene protein encoded by HTLV-1 has never been reported until now.